Article records
https://feeds.library.caltech.edu/people/Chung-Soon-Jo/article.rss
A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenMon, 15 Apr 2024 23:26:52 +0000ARGOS testbed: study of multidisciplinary challenges of future spaceborne interferometric arrays
https://resolver.caltech.edu/CaltechAUTHORS:20161130-102813056
Authors: {'items': [{'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Miller-D-W', 'name': {'family': 'Miller', 'given': 'David W.'}, 'orcid': '0000-0001-6099-0614'}, {'id': 'de-Weck-O-L', 'name': {'family': 'de Weck', 'given': 'Olivier L.'}}]}
Year: 2004
DOI: 10.1117/1.1779232
Future spaceborne interferometric arrays must meet stringent optical performance and tolerance requirements while exhibiting modularity and acceptable manufacture and integration cost levels. The Massachusetts Institute of Technology (MIT) Adaptive Reconnaissance Golay-3 Optical Satellite (ARGOS) is a wide-angle Fizeau interferometer spacecraft testbed designed to address these research challenges. Designing a space-based stellar interferometer, which requires tight tolerances on pointing and alignment for its apertures, presents unique multidisciplinary challenges in the areas of structural dynamics, controls, and multiaperture phasing active optics. In meeting these challenges, emphasis is placed on modularity in spacecraft subsystems and optics as a means of enabling expandability and upgradeability. A rigorous theory of beam-combining errors for sparse optical arrays is derived and flown down to the design of various subsystems. A detailed elaboration on the optics system and control system is presented based on the performance requirements and beam-combining error tolerances. The space environment is simulated by floating ARGOS on a frictionless air-bearing that enables it to track both fast and slow moving targets.https://authors.library.caltech.edu/records/mwadb-5xe08Nonlinear Model Reduction and Decentralized Control of Tethered Formation Flight
https://resolver.caltech.edu/CaltechAUTHORS:20161221-150412096
Authors: {'items': [{'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Slotine-J-J-E', 'name': {'family': 'Slotine', 'given': 'Jean-Jacques E.'}}, {'id': 'Miller-D-W', 'name': {'family': 'Miller', 'given': 'David W.'}, 'orcid': '0000-0001-6099-0614'}]}
Year: 2007
DOI: 10.2514/1.21492
This paper describes a fully decentralized nonlinear control law for spinning tethered formation flight, based on
exploiting geometric symmetries to reduce the original nonlinear dynamics into simpler stable dynamics. Motivated
by oscillation synchronization in biological systems, we use contraction theory to prove that a control law stabilizing a single-tethered spacecraft can also stabilize arbitrary large circular arrays of spacecraft, as well as the three inline configuration. The convergence result is global and exponential. Numerical simulations and experimental results using the SPHERES testbed validate the exponential stability of the tethered formation arrays by implementing a tracking control law derived from the reduced dynamics.https://authors.library.caltech.edu/records/fdees-cnz24Propellant-Free Control of Tethered Formation Flight, Part 1: Linear Control and Experimentation
https://resolver.caltech.edu/CaltechAUTHORS:20161221-082116459
Authors: {'items': [{'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Miller-D-W', 'name': {'family': 'Miller', 'given': 'David W.'}, 'orcid': '0000-0001-6099-0614'}]}
Year: 2008
DOI: 10.2514/1.32188
We introduce a decentralized attitude control strategy that can dramatically reduce the usage of propellant, by
taking full advantage of the physical coupling of the tether. Motivated by a controllability analysis, indicating that both array resizing and spin-up are fully controllable by the reaction wheels and the tether motor, we report the first propellant-free underactuated control results for tethered formation flying spacecraft. This paper also describes the hardware development and experimental validation of the proposed method using the Synchronized Position Hold, Engage, and Reorient Experimental Satellites test bed. In particular, a new relative sensing mechanism that uses sixderee-of-freedom force-torque sensors and rate gyroscopes is introduced and validated in the closed-loop control experiments.https://authors.library.caltech.edu/records/1spze-hrk49Propellant-Free Control of Tethered Formation Flight, Part 2: Nonlinear Underactuated Control
https://resolver.caltech.edu/CaltechAUTHORS:20161221-084003468
Authors: {'items': [{'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Slotine-J-J-E', 'name': {'family': 'Slotine', 'given': 'Jean-Jacques E.'}}, {'id': 'Miller-D-W', 'name': {'family': 'Miller', 'given': 'David W.'}, 'orcid': '0000-0001-6099-0614'}]}
Year: 2008
DOI: 10.2514/1.32189
This is the second in a series of papers that exploit the physical coupling of tethered spacecraft to derive a
propellant-free spin-up and attitude control strategy. We take a nonlinear control approach to underactuated
tethered formation flying spacecraft, whose lack of full state feedback linearizability, along with their complex
nonholonomic behavior, characterizes the difficult nonlinear control problem. We introduce several nonlinear
control laws that are more efficient in tracking time-varying trajectories than linear control. We also extend our
decentralized control approach to underactuated tethered systems, thereby eliminating the need for any intersatellite
communication. To our knowledge, this work reports the first nonlinear control results for underactuated tethered
formation flying spacecraft. This article further illustrates the potential of the proposed strategy by providing a new
momentum dumping method that does not use torque-generating thrusters.https://authors.library.caltech.edu/records/0dm8s-zq764Application of Synchronization to Formation Flying Spacecraft: Lagrangian Approach
https://resolver.caltech.edu/CaltechAUTHORS:20161130-073636281
Authors: {'items': [{'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Ahsun-Umair', 'name': {'family': 'Ahsun', 'given': 'Umair'}}, {'id': 'Slotine-J-J-E', 'name': {'family': 'Slotine', 'given': 'Jean-Jacques E.'}}]}
Year: 2009
DOI: 10.2514/1.37261
This paper presents a unified synchronization framework with application to precision formation flying
spacecraft. Central to the proposed innovation, in applying synchronization to both translational and rotational
dynamics in the Lagrangian form, is the use of the distributed stability and performance analysis tool, called
contraction analysis that yields exact nonlinear stability proofs. The proposed decentralized tracking control law
synchronizes the attitude of an arbitrary number of spacecraft into a common time-varying trajectory with global
exponential convergence. Moreover, a decentralized translational tracking control law based on oscillator phase
synchronization is presented, thus enabling coupled translational and rotational maneuvers. Although the
translational dynamics can be adequately controlled by linear control laws, the proposed method permits highly
nonlinear systems with nonlinearly coupled inertia matrices such as the attitude dynamics of spacecraft whose large
and rapid slew maneuvers justify the nonlinear control approach. The proposed method integrates both the
trajectory tracking and synchronization problems in a single control framework.https://authors.library.caltech.edu/records/095f3-qd460Cooperative Robot Control and Concurrent Synchronization of Lagrangian Systems
https://resolver.caltech.edu/CaltechAUTHORS:20161128-090602956
Authors: {'items': [{'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Slotine-J-J-E', 'name': {'family': 'Slotine', 'given': 'Jean-Jacques E.'}}]}
Year: 2009
DOI: 10.1109/TRO.2009.2014125
Concurrent synchronization is a regime where diverse groups of fully synchronized dynamic systems stably coexist. We study global exponential synchronization and concurrent synchronization in the context of Lagrangian systems control. In a network constructed by adding diffusive couplings to robot manipulators or mobile robots, a decentralized tracking control law globally exponentially synchronizes an arbitrary number of robots, and represents a generalization of the average consensus problem. Exact nonlinear stability guarantees and synchronization conditions are derived by contraction analysis. The proposed decentralized strategy is further extended to adaptive synchronization and partial-state coupling.https://authors.library.caltech.edu/records/qzwfd-a9g27Neurobiologically Inspired Control of Engineered Flapping Flight
https://resolver.caltech.edu/CaltechAUTHORS:20161130-101815580
Authors: {'items': [{'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Dorothy-M-R', 'name': {'family': 'Dorothy', 'given': 'Michael'}}]}
Year: 2010
DOI: 10.2514/1.45311
This paper presents a new control approach and a dynamic model for engineered flapping flight with many interacting degrees of freedom. This paper explores the applications of neurobiologically inspired control systems in the form of central pattern generators to control flapping-flight dynamics. A rigorous mathematical and control theoretic framework to design complex three-dimensional wing motions is presented based on phase synchronization of nonlinear oscillators. In particular, we show that flapping-flying dynamics without a tail or traditional aerodynamic control surfaces can be effectively controlled by a reduced set of central pattern generator parameters that generate phase-synchronized or symmetry-breaking oscillatory motions of two main wings. Furthermore, by using Hopf bifurcation, we show that tailless aircraft alternating between flapping and gliding can be effectively stabilized by smooth wing motions driven by the central pattern generator network. Results of numerical simulation with a full six-degree-of-freedom flight dynamic model validate the effectiveness of the proposed neurobiologically inspired control approach.https://authors.library.caltech.edu/records/whm3y-sx593CPG-based control of a turtle-like underwater vehicle
https://resolver.caltech.edu/CaltechAUTHORS:20161130-142353121
Authors: {'items': [{'id': 'Seo-Keehong', 'name': {'family': 'Seo', 'given': 'Keehong'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Slotine-J-J-E', 'name': {'family': 'Slotine', 'given': 'Jean-Jacques E.'}}]}
Year: 2010
DOI: 10.1007/s10514-009-9169-0
This paper presents biologically inspired control strategies for an autonomous underwater vehicle (AUV) propelled by flapping fins that resemble the paddle-like forelimbs of a sea turtle. Our proposed framework exploits limit cycle oscillators and diffusive couplings, thereby constructing coupled nonlinear oscillators, similar to the central pattern generators (CPGs) in animal spinal cords. This paper first presents rigorous stability analyses and experimental results of CPG-based control methods with and without actuator feedback to the CPG. In these methods, the CPG module generates synchronized oscillation patterns, which are sent to position-servoed flapping fin actuators as a reference input. In order to overcome the limitation of the open-loop CPG that the synchronization is occurring only between the reference signals, this paper introduces a new single-layered CPG method, where the CPG and the physical layers are combined as a single layer, to ensure the synchronization of the physical actuators in the presence of external disturbances. The key idea is to replace nonlinear oscillators in the conventional CPG models with physical actuators that oscillate due to nonlinear state feedback of the actuator states. Using contraction theory, a relatively new nonlinear stability tool, we show that coupled nonlinear oscillators globally synchronize to a specific pattern that can be stereotyped by an outer-loop controller. Results of experimentation with a turtle-like AUV show the feasibility of the proposed control laws.https://authors.library.caltech.edu/records/kjnrb-acq76Flight mechanics of a tailless articulated wing aircraft
https://resolver.caltech.edu/CaltechAUTHORS:20161220-140818336
Authors: {'items': [{'id': 'Paranjape-A-A', 'name': {'family': 'Paranjape', 'given': 'Aditya A.'}, 'orcid': '0000-0002-3164-3215'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Selig-M-S', 'name': {'family': 'Selig', 'given': 'Michael S.'}}]}
Year: 2011
DOI: 10.1088/1748-3182/6/2/026005
This paper investigates the flight mechanics of a micro aerial vehicle without a vertical tail in an effort to reverse-engineer the agility of avian flight. The key to stability and control of such a tailless aircraft lies in the ability to control the incidence angles and dihedral angles of both wings independently. The dihedral angles can be varied symmetrically on both wings to control aircraft speed independently of the angle of attack and flight path angle, while asymmetric dihedral can be used to control yaw in the absence of a vertical stabilizer. It is shown that wing dihedral angles alone can effectively regulate sideslip during rapid turns and generate a wide range of equilibrium turn rates while maintaining a constant flight speed and regulating sideslip. Numerical continuation and bifurcation analysis are used to compute trim states and assess their stability. This paper lays the foundation for design and stability analysis of a flapping wing aircraft that can switch rapidly from flapping to gliding flight for agile manoeuvring in a constrained environment.https://authors.library.caltech.edu/records/571qq-sx069Dynamics and Performance of Tailless Micro Aerial Vehicle with Flexible Articulated Wings
https://resolver.caltech.edu/CaltechAUTHORS:20161130-095641127
Authors: {'items': [{'id': 'Paranjape-A-A', 'name': {'family': 'Paranjape', 'given': 'Aditya A.'}, 'orcid': '0000-0002-3164-3215'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hilton-H-H', 'name': {'family': 'Hilton', 'given': 'Harry H.'}}, {'id': 'Chakravarthy-A', 'name': {'family': 'Chakravarthy', 'given': 'Animesh'}}]}
Year: 2012
DOI: 10.2514/1.J051447
The purpose of this paper is to analyze and discuss the performance and stability of a tailless micro aerial vehicle
with flexible articulated wings. The dihedral angles can be varied symmetrically on both wings to control the aircraft
speed independently of the angle of attack and flight-path angle, while an asymmetric dihedral setting can be used to
control yaw in the absence of a vertical tail.Anonlinear aero-elastic model is derived, and it is used to study the steady-state performance and flight stability of the micro aerial vehicle. The concept of the effective dihedral is introduced, which allows for a unified treatment of rigid and flexible wing aircraft. It also identifies the amount of elasticity that is necessary to obtain tangible performance benefits over a rigid wing. The feasibility of using axial tension to stiffen the wing is discussed, and, at least in the context of a linear model, it is shown that adding axial tension is effective but undesirable. The turning performance of an micro aerial vehicle with flexible wings is compared to an otherwise identical micro aerial vehicle with rigid wings. The wing dihedral alone can be varied asymmetrically to perform rapid turns and regulate sideslip. The maximum attainable turn rate for a given elevator setting, however, does not increase unless antisymmetric wing twisting is employed.https://authors.library.caltech.edu/records/6tg3z-jcz30A Flight Mechanics-Centric Review of Bird-Scale Flapping Flight
https://resolver.caltech.edu/CaltechAUTHORS:20161221-114021846
Authors: {'items': [{'id': 'Paranjape-A-A', 'name': {'family': 'Paranjape', 'given': 'Aditya A.'}, 'orcid': '0000-0002-3164-3215'}, {'id': 'Dorothy-M-R', 'name': {'family': 'Dorothy', 'given': 'Michael R.'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Lee-Ki-D', 'name': {'family': 'Lee', 'given': 'Ki-D.'}}]}
Year: 2012
DOI: 10.5139/IJASS.2012.13.3.267
This paper reviews the flight mechanics and control of birds and bird-size aircraft. It is intended to fill a niche in the current survey literature which focuses primarily on the aerodynamics, flight dynamics and control of insect scale flight. We review the flight mechanics from first principles and summarize some recent results on the stability and control of birds and bird-scale aircraft. Birds spend a considerable portion of their flight in the gliding (i.e., non-flapping) phase. Therefore, we also review the stability and control of gliding flight, and particularly those aspects which are derived from the unique control features of birds.https://authors.library.caltech.edu/records/vt1e0-tpe48Swarm-Keeping Strategies for Spacecraft Under J_2 and Atmospheric Drag Perturbations
https://resolver.caltech.edu/CaltechAUTHORS:20170123-081714017
Authors: {'items': [{'id': 'Morgan-Daniel', 'name': {'family': 'Morgan', 'given': 'Daniel'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Blackmore-L', 'name': {'family': 'Blackmore', 'given': 'Lars'}}, {'id': 'Acikmese-B', 'name': {'family': 'Acikmese', 'given': 'Behcet'}}, {'id': 'Bayard-D-S', 'name': {'family': 'Bayard', 'given': 'David'}}, {'id': 'Hadaegh-F-Y', 'name': {'family': 'Hadaegh', 'given': 'Fred Y.'}}]}
Year: 2012
DOI: 10.2514/1.55705
This paper presents several new open-loop guidance methods for spacecraft swarms composed of hundreds to thousands of agents, with each spacecraft having modest capabilities. These methods have three main goals: preventing relative drift of the swarm, preventing collisions within the swarm, and minimizing the propellant used throughout the mission. The development of these methods progresses by eliminating drift using the Hill–Clohessy–Wiltshire equations, removing drift due to nonlinearity, and minimizing the J_2 drift. To verify these guidance methods, a new dynamic model for the relative motion of spacecraft is developed. These dynamics include the two main disturbances for spacecraft in low Earth orbit, J_2 and atmospheric drag. Using this dynamic model, numerical
simulations are provided at each step to show the effectiveness of each method and to see where improvements can be made. The main result is a set of initial conditions for each spacecraft in the swarm, which provides the trajectories for hundreds of collision-free orbits in the presence of J_2. Finally, a multiburn strategy is developed to provide hundreds of collision-free orbits under the influence of atmospheric drag. This last method works by enforcing the initial conditions multiple times throughout the mission, thereby providing collision-free trajectories for the duration of the mission.https://authors.library.caltech.edu/records/6kn9p-a5y93Phase synchronization control of complex networks of Lagrangian systems on adaptive digraphs
https://resolver.caltech.edu/CaltechAUTHORS:20161130-141734262
Authors: {'items': [{'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Bandyopadhyay-S', 'name': {'family': 'Bandyopadhyay', 'given': 'Saptarshi'}}, {'id': 'Chang-Insu', 'name': {'family': 'Chang', 'given': 'Insu'}}, {'id': 'Hadaegh-F-Y', 'name': {'family': 'Hadaegh', 'given': 'Fred Y.'}}]}
Year: 2013
DOI: 10.1016/j.automatica.2013.01.048
This paper presents a formation control and synchronization method that utilizes adaptive network topologies for a class of complex dynamical networks comprised of a large number of highly-nonlinear Euler–Lagrange (EL) systems. A time-varying and switching network topology, constructed by the adaptive graph Laplacian matrix, relaxes the standard requirement of consensus stability, even permitting exponential synchronization on an unbalanced digraph or a weakly connected digraph that can sporadically lose connectivity. The time-varying graph Laplacian matrix is adapted by an adaptive control scheme based on relative positions and errors of synchronization and tracking. The adaptive graph Laplacian is integrated with a phase synchronization controller that synchronizes the relative motions of EL systems moving in elliptical orbits, thereby yielding a smaller synchronization error than an uncoupled tracking control law in the presence of bounded disturbances and modeling errors. An example of reconfiguring hundreds of spacecraft in Low Earth Orbit shows the effectiveness of the proposed phase synchronization controller for a large number of complex EL systems moving in periodic elliptical orbits.https://authors.library.caltech.edu/records/k1mgf-hp379PDE Boundary Control for Flexible Articulated Wings on a Robotic Aircraft
https://resolver.caltech.edu/CaltechAUTHORS:20161123-113256359
Authors: {'items': [{'id': 'Paranjape-A-A', 'name': {'family': 'Paranjape', 'given': 'Aditya A.'}, 'orcid': '0000-0002-3164-3215'}, {'id': 'Guan-Jinyu', 'name': {'family': 'Guan', 'given': 'Jinyu'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Krstic-Miroslav', 'name': {'family': 'Krstic', 'given': 'Miroslav'}}]}
Year: 2013
DOI: 10.1109/TRO.2013.224071
This paper presents a boundary control formulation for distributed parameter systems described by partial differential equations (PDEs) and whose output is given by a spatial integral of weighted functions of the state. This formulation is directly applicable to the control of small robotic aircraft with articulated flexible wings, where the output of interest is the net aerodynamic force or moment. The deformation of flexible wings can be controlled by actuators that are located at the root or the tip of the wing. The problem of designing a tracking controller for wing twist is addressed using a combination of PDE backstepping for feedback stabilization and feed-forward trajectory planning. We also design an adaptive tracking controller for wing tip actuators. For wing bending, we present a novel control scheme that is based on a two-stage perturbation observer. A trajectory planning-based feed-forward tracker is designed using only one component of the observer whose dynamics are homogeneous and amenable to trajectory planning. The two components, put together, estimate the external forces and unmodeled system dynamics. The effectiveness of the proposed controllers for twist and bending is demonstrated by simulations. This paper also reports experimental validation of the perturbation-observer-based controller for beam bending.https://authors.library.caltech.edu/records/s9bdf-c4k35Novel Dihedral-Based Control of Flapping-Wing Aircraft With Application to Perching
https://resolver.caltech.edu/CaltechAUTHORS:20161222-073434259
Authors: {'items': [{'id': 'Paranjape-A-A', 'name': {'family': 'Paranjape', 'given': 'Aditya A.'}, 'orcid': '0000-0002-3164-3215'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Kim-Joseph', 'name': {'family': 'Kim', 'given': 'Joseph'}}]}
Year: 2013
DOI: 10.1109/TRO.2013.2268947
We describe the design of an aerial robot inspired by birds and the underlying theoretical developments leading to novel control and closed-loop guidance algorithms for a perching maneuver. A unique feature of this robot is that it uses wing articulation to control the flight path angle as well as the heading angle. It lacks a vertical tail for improved agility, which results in unstable lateral-directional dynamics. New closed-loop motion planning algorithms with guaranteed stability are obtained by rewriting the flight dynamic equations in the spatial domain rather than as functions of time, after which dynamic inversion is employed. It is shown that nonlinear dynamic inversion naturally leads to proportional-integral-derivative controllers, thereby providing an exact method for tuning the gains. The capabilities of the proposed bioinspired robot design and its novel closed-loop perching controller have been successfully demonstrated with perched landings on a human hand.https://authors.library.caltech.edu/records/637t0-68r89Model Predictive Control of Swarms of Spacecraft Using Sequential Convex Programming
https://resolver.caltech.edu/CaltechAUTHORS:20161221-145205891
Authors: {'items': [{'id': 'Morgan-Daniel', 'name': {'family': 'Morgan', 'given': 'Daniel'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hadaegh-F-Y', 'name': {'family': 'Hadaegh', 'given': 'Fred Y.'}}]}
Year: 2014
DOI: 10.2514/1.G000218
This paper presents a decentralized, model predictive control algorithm for the optimal guidance and reconfiguration of swarms of spacecraft composed of hundreds to thousands of agents with limited capabilities. In previous work, J_2-invariant orbits have been found to provide collision-free motion for hundreds of orbits in a low Earth orbit. This paper develops real-time optimal control algorithms for the swarm reconfiguration that involve transferring from one J_2-invariant orbit to another while avoiding collisions and minimizing fuel. The proposed model predictive control-sequential convex programming algorithm uses sequential convex programming to solve a series of approximate path planning problems until the solution converges. By updating the optimal trajectories during the reconfiguration, the model predictive control algorithm results in decentralized computations and communication between neighboring spacecraft only. Additionally, model predictive control reduces the horizon of the convex optimizations, which reduces the run time of the algorithm. Multiple time steps, time-varying collision constraints, and communication requirements are developed to guarantee stability, feasibility, and robustness of the model predictive control-sequential convex programming algorithm.https://authors.library.caltech.edu/records/mvhns-j8a04Target Assignment in Robotic Networks: Distance Optimality Guarantees and Hierarchical Strategies
https://resolver.caltech.edu/CaltechAUTHORS:20161221-081248601
Authors: {'items': [{'id': 'Yu-Jingjin', 'name': {'family': 'Yu', 'given': 'Jingjin'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Voulgaris-P-G', 'name': {'family': 'Voulgaris', 'given': 'Petros G.'}}]}
Year: 2015
DOI: 10.1109/TAC.2014.2344291
We study the problem of multi-robot target assignment to minimize the total distance traveled by the robots until they all reach an equal number of static targets. In the first half of the paper, we present a necessary and sufficient condition under which true distance optimality can be achieved for robots with limited communication and target-sensing ranges. Moreover, we provide an explicit, non-asymptotic formula for computing the number of robots needed to achieve distance optimality in terms of the robots' communication and target-sensing ranges with arbitrary guaranteed probabilities. The same bounds are also shown to be asymptotically tight. In the second half of the paper, we present suboptimal strategies for use when the number of robots cannot be chosen freely. Assuming first that all targets are known to all robots, we employ a hierarchical communication model in which robots communicate only with other robots in the same partitioned region. This hierarchical communication model leads to constant approximations of true distance-optimal solutions under mild assumptions. We then revisit the limited communication and sensing models. By combining simple rendezvous-based strategies with a hierarchical communication model, we obtain decentralized hierarchical strategies that achieve constant approximation ratios with respect to true distance optimality. Results of simulation show that the approximation ratio is as low as 1.4.https://authors.library.caltech.edu/records/6qdrh-jf726Motion primitives and 3D path planning for fast flight through a forest
https://resolver.caltech.edu/CaltechAUTHORS:20161221-115331865
Authors: {'items': [{'id': 'Paranjape-A-A', 'name': {'family': 'Paranjape', 'given': 'Aditya A.'}, 'orcid': '0000-0002-3164-3215'}, {'id': 'Meier-K-C', 'name': {'family': 'Meier', 'given': 'Kevin C.'}, 'orcid': '0000-0003-4000-1422'}, {'id': 'Shi-Xichen', 'name': {'family': 'Shi', 'given': 'Xichen'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hutchinson-Seth', 'name': {'family': 'Hutchinson', 'given': 'Seth'}, 'orcid': '0000-0002-3949-6061'}]}
Year: 2015
DOI: 10.1177/0278364914558017
This paper presents two families of motion primitives for enabling fast, agile flight through a dense obstacle field. The first family of primitives consists of a time-delay dependent 3D circular path between two points in space and the control inputs required to fly the path. In particular, the control inputs are calculated using algebraic equations which depend on the flight parameters and the location of the waypoint. Moreover, the transition between successive maneuver states, where each state is defined by a unique combination of constant control inputs, is modeled rigorously as an instantaneous switch between the two maneuver states following a time delay which is directly related to the agility of the robotic aircraft. The second family consists of aggressive turn-around (ATA) maneuvers which the robot uses to retreat from impenetrable pockets of obstacles. The ATA maneuver consists of an orchestrated sequence of three sets of constant control inputs. The duration of the first segment is used to optimize the ATA for the spatial constraints imposed by the turning volume. The motion primitives are validated experimentally and implemented in a simulated receding horizon control (RHC)-based motion planner. The paper concludes with inverse-design pointers derived from the primitives.https://authors.library.caltech.edu/records/kgw1m-9df45Observer Design for Stochastic Nonlinear Systems via Contraction-Based Incremental Stability
https://resolver.caltech.edu/CaltechAUTHORS:20170123-080211922
Authors: {'items': [{'id': 'Dani-Ashwin-P', 'name': {'family': 'Dani', 'given': 'Ashwin P.'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hutchinson-Seth', 'name': {'family': 'Hutchinson', 'given': 'Seth'}, 'orcid': '0000-0002-3949-6061'}]}
Year: 2015
DOI: 10.1109/TAC.2014.2357671
This paper presents a new design approach to nonlinear observers for Itô stochastic nonlinear systems with guaranteed stability. A stochastic contraction lemma is presented which is used to analyze incremental stability of the observer. A bound on the mean-squared distance between the trajectories of original dynamics and the observer dynamics is obtained as a function of the contraction rate and maximum noise intensity. The observer design is based on a non-unique state-dependent coefficient (SDC) form, which parametrizes the nonlinearity in an extended linear form. The observer gain synthesis algorithm, called linear matrix inequality state-dependent algebraic Riccati equation (LMI-SDARE), is presented. The LMI-SDARE uses a convex combination of multiple SDC parametrizations. An optimization problem with state-dependent linear matrix inequality (SDLMI) constraints is formulated to select the coefficients of the convex combination for maximizing the convergence rate and robustness against disturbances. Two variations of LMI-SDARE algorithm are also proposed. One of them named convex state-dependent Riccati equation (CSDRE) uses a chosen convex combination of multiple SDC matrices; and the other named Fixed-SDARE uses constant SDC matrices that are pre-computed by using conservative bounds of the system states while using constant coefficients of the convex combination pre-computed by a convex LMI optimization problem. A connection between contraction analysis and L_2 gain of the nonlinear system is established in the presence of noise and disturbances. Results of simulation show superiority of the LMI-SDARE algorithm to the extended Kalman filter (EKF) and state-dependent differential Riccati equation (SDDRE) filter.https://authors.library.caltech.edu/records/jv1cz-00v81Swarm assignment and trajectory optimization using variable-swarm, distributed auction assignment and sequential convex programming
https://resolver.caltech.edu/CaltechAUTHORS:20161221-120751793
Authors: {'items': [{'id': 'Morgan-Daniel', 'name': {'family': 'Morgan', 'given': 'Daniel'}}, {'id': 'Subramanian-G-P', 'name': {'family': 'Subramanian', 'given': 'Giri P.'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hadaegh-F-Y', 'name': {'family': 'Hadaegh', 'given': 'Fred Y.'}}]}
Year: 2016
DOI: 10.1177/0278364916632065
This paper presents a distributed, guidance and control algorithm for reconfiguring swarms composed of hundreds to thousands of agents with limited communication and computation capabilities. This algorithm solves both the optimal assignment and collision-free trajectory generation for robotic swarms, in an integrated manner, when given the desired shape of the swarm (without pre-assigned terminal positions). The optimal assignment problem is solved using a distributed auction assignment that can vary the number of target positions in the assignment, and the collision-free trajectories are generated using sequential convex programming. Finally, model predictive control is used to solve the assignment and trajectory generation in real time using a receding horizon. The model predictive control formulation uses current state measurements to resolve for the optimal assignment and trajectory. The implementation of the distributed auction algorithm and sequential convex programming using model predictive control produces the swarm assignment and trajectory optimization (SATO) algorithm that transfers a swarm of robots or vehicles to a desired shape in a distributed fashion. Once the desired shape is uploaded to the swarm, the algorithm determines where each robot goes and how it should get there in a fuel-efficient, collision-free manner. Results of flight experiments using multiple quadcopters show the effectiveness of the proposed SATO algorithm.https://authors.library.caltech.edu/records/pfd7q-4hd57Nonlinear Attitude Control of Spacecraft with a Large Captured Object
https://resolver.caltech.edu/CaltechAUTHORS:20161108-150956974
Authors: {'items': [{'id': 'Bandyopadhyay-S', 'name': {'family': 'Bandyopadhyay', 'given': 'Saptarshi'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hadaegh-F-Y', 'name': {'family': 'Hadaegh', 'given': 'Fred Y.'}}]}
Year: 2016
DOI: 10.2514/1.G001341
This paper presents an attitude control strategy and a new nonlinear tracking controller for a spacecraft carrying a large object, such as an asteroid or a boulder. If the captured object is larger or comparable in size to the spacecraft and has significant modeling uncertainties, conventional nonlinear control laws that use exact feedforward cancellation are not suitable because they exhibit a large resultant disturbance torque. The proposed nonlinear tracking control law guarantees global exponential convergence of tracking errors with finite-gain L_p stability in the presence of modeling uncertainties and disturbances, and it reduces the resultant disturbance torque. Furthermore, this control law permits the use of any attitude representation, and its integral control formulation eliminates any constant disturbance. Under small uncertainties, the best strategy for stabilizing the combined system is to track a fuel-optimal reference trajectory using this nonlinear control law because it consumes the least amount of fuel. In the presence of large uncertainties, the most effective strategy is to track the derivative plus proportional–derivative-based reference trajectory because it reduces the resultant disturbance torque. The effectiveness of the proposed attitude control methods is demonstrated by using results of numerical simulation based on an Asteroid Redirect Mission concept.https://authors.library.caltech.edu/records/w3tkf-63h38Review of Formation Flying and Constellation Missions Using Nanosatellites
https://resolver.caltech.edu/CaltechAUTHORS:20161108-143406692
Authors: {'items': [{'id': 'Bandyopadhyay-S', 'name': {'family': 'Bandyopadhyay', 'given': 'Saptarshi'}}, {'id': 'Foust-Rebecca', 'name': {'family': 'Foust', 'given': 'Rebecca'}, 'orcid': '0000-0003-1470-1716'}, {'id': 'Subramanian-G-P', 'name': {'family': 'Subramanian', 'given': 'Giri P.'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hadaegh-F-Y', 'name': {'family': 'Hadaegh', 'given': 'Fred Y.'}}]}
Year: 2016
DOI: 10.2514/1.A33291
Small satellites are enabling multisatellite missions that were not otherwise possible because of their small size and modular nature [1]. Multiple small satellites can be flown instead of a much bigger and costlier conventional satellite for distributed sensing applications such as atmospheric sampling, distributed antennas [2], and synthetic apertures [3,4]. Missions with multiple small satellites can deliver a comparable or greater mission capability than a monolithic satellite, but with significantly enhanced flexibility (adaptability, scalability, evolvability, and maintainability) and robustness (reliability, survivability, and fault tolerance) [1,5]. Small satellites that weigh less than 10 kg can be broadly classified into nanosatellites (mass between 1 and 10 kg), picosatellites (mass between 0.1 and 1 kg), and femtosatellites (mass less than 100 g) [1,6].https://authors.library.caltech.edu/records/grvp5-kkn14On Development of 100-Gram-Class Spacecraft for Swarm Applications
https://resolver.caltech.edu/CaltechAUTHORS:20161220-151103451
Authors: {'items': [{'id': 'Hadaegh-F-Y', 'name': {'family': 'Hadaegh', 'given': 'Fred Y.'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Manohara-Harish-M', 'name': {'family': 'Manohara', 'given': 'Harish M.'}}]}
Year: 2016
DOI: 10.1109/JSYST.2014.2327972
A novel space system architecture is proposed, which would enable 100-g-class spacecraft to be flown as swarms (100 s-1000 s) in low Earth orbit. Swarms of Silicon Wafer Integrated Femtosatellites (SWIFT) present a paradigm-shifting approach to distributed spacecraft development, missions, and applications. Potential applications of SWIFT swarms include sparse aperture arrays and distributed sensor networks. New swarm array configurations are introduced and shown to achieve the effective sparse aperture driven from optical performance metrics. A system cost analysis based on this comparison justifies deploying a large number of femtosatellites for sparse aperture applications. Moreover, this paper discusses promising guidance, control, and navigation methods for swarms of femtosatellites equipped with modest sensing and control capabilities.https://authors.library.caltech.edu/records/h7bp9-r5c83Switched systems with multiple invariant sets
https://resolver.caltech.edu/CaltechAUTHORS:20161220-142210479
Authors: {'items': [{'id': 'Dorothy-M-R', 'name': {'family': 'Dorothy', 'given': 'Michael'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2016
DOI: 10.1016/j.sysconle.2016.07.008
This paper explores dwell time constraints on switched systems with multiple, possibly disparate invariant limit sets. We show that, under suitable conditions, trajectories globally converge to a superset of the limit sets and then remain in a second, larger superset. We show the effectiveness of the dwell-time conditions by using examples of switching limit cycles commonly found in robotic locomotion and flapping flight.https://authors.library.caltech.edu/records/eq88m-jha24A biomimetic robotic platform to study flight specializations of bats
https://resolver.caltech.edu/CaltechAUTHORS:20170201-201024724
Authors: {'items': [{'id': 'Ramezani-Alireza', 'name': {'family': 'Ramezani', 'given': 'Alireza'}, 'orcid': '0000-0002-3391-5288'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hutchinson-Seth', 'name': {'family': 'Hutchinson', 'given': 'Seth'}, 'orcid': '0000-0002-3949-6061'}]}
Year: 2017
DOI: 10.1126/scirobotics.aal2505
Bats have long captured the imaginations of scientists and engineers with their unrivaled agility and maneuvering characteristics, achieved by functionally versatile dynamic wing conformations as well as more than 40 active and passive joints on the wings. Wing flexibility and complex wing kinematics not only bring a unique perspective to research in biology and aerial robotics but also pose substantial technological challenges for robot modeling, design, and control. We have created a fully self-contained, autonomous flying robot that weighs 93 grams, called Bat Bot (B2), to mimic such morphological properties of bat wings. Instead of using a large number of distributed control actuators, we implement highly stretchable silicone-based membrane wings that are controlled at a reduced number of dominant wing joints to best match the morphological characteristics of bat flight. First, the dominant degrees of freedom (DOFs) in the bat flight mechanism are identified and incorporated in B2's design by means of a series of mechanical constraints. These biologically meaningful DOFs include asynchronous and mediolateral movements of the armwings and dorsoventral movements of the legs. Second, the continuous surface and elastic properties of bat skin under wing morphing are realized by an ultrathin (56 micrometers) membranous skin that covers the skeleton of the morphing wings. We have successfully achieved autonomous flight of B2 using a series of virtual constraints to control the articulated, morphing wings.https://authors.library.caltech.edu/records/a0z34-waf85Vision-based Localization and Robot-centric Mapping in Riverine Environments
https://resolver.caltech.edu/CaltechAUTHORS:20170117-075620944
Authors: {'items': [{'id': 'Yang-Junho', 'name': {'family': 'Yang', 'given': 'Junho'}}, {'id': 'Dani-Ashwin-P', 'name': {'family': 'Dani', 'given': 'Ashwin P.'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hutchinson-Seth', 'name': {'family': 'Hutchinson', 'given': 'Seth'}, 'orcid': '0000-0002-3949-6061'}]}
Year: 2017
DOI: 10.1002/rob.21606
This paper presents a vision-based localization and mapping algorithm developed for an unmanned aerial vehicle (UAV) that can operate in a riverine environment. Our algorithm estimates the three-dimensional positions of point features along a river and the pose of the UAV. By detecting features surrounding a river and the corresponding reflections on the water's surface, we can exploit multiple-view geometry to enhance the observability of the estimation system. We use a robot-centric mapping framework to further improve the observability of the estimation system while reducing the computational burden. We analyze the performance of the proposed algorithm with numerical simulations and demonstrate its effectiveness through experiments with data from Crystal Lake Park in Urbana, Illinois. We also draw a comparison to existing approaches. Our experimental platform is equipped with a lightweight monocular camera, an inertial measurement unit, a magnetometer, an altimeter, and an onboard computer. To our knowledge, this is the first result that exploits the reflections of features in a riverine environment for localization and mapping.https://authors.library.caltech.edu/records/5r3q9-brx12Co-Design of Strain-Actuated Solar Arrays for Spacecraft Precision Pointing and Jitter Reduction
https://resolver.caltech.edu/CaltechAUTHORS:20170523-113653804
Authors: {'items': [{'id': 'Chilan-Christian-M', 'name': {'family': 'Chilan', 'given': 'Christian M.'}}, {'id': 'Herber-Daniel-R', 'name': {'family': 'Herber', 'given': 'Daniel R.'}}, {'id': 'Nakka-Yashwanth-K', 'name': {'family': 'Nakka', 'given': 'Yashwanth Kumar'}, 'orcid': '0000-0001-7897-3644'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Allison-James-T', 'name': {'family': 'Allison', 'given': 'James T.'}}, {'id': 'Aldrich-Jack-B', 'name': {'family': 'Aldrich', 'given': 'Jack B.'}}, {'id': 'Alvarez-Salazar-O-S', 'name': {'family': 'Alvarez-Salazar', 'given': 'Oscar S.'}}]}
Year: 2017
DOI: 10.2514/1.J055748
This work presents a novel spacecraft attitude control architecture using strain-actuated solar arrays that does not require the use of conventional attitude control hardware. A strain-actuated solar array enables attitude slewing maneuvers and precision pointing (image acquisition) stares, while simultaneously suppressing structural vibrations. Distributed piezoelectric actuators help achieve higher precision, higher bandwidth, and quieter operation than reaction wheels. To understand the design tradeoffs for this architecture, a framework for the integrated design of distributed structural geometry and distributed control is presented. The physical properties of the array are modeled and designed with respect to a piecewise linear distributed thickness profile. The distributed control is a voltage profile across the array modeled as a spatially continuous function. The dynamics of the system are modeled using a coupled ordinary differential equation–partial differential equation system using extended generalizations for hybrid coordinate systems. The combined physical and control system design, or co-design problem is investigated to understand the optimal performance of the system. Single-axis slew maneuvers of 7.2 milliradians or 1485 arcsec are achieved for a representative spacecraft model without increasing array mass or reducing array planform area. From additional tradeoff studies, a design criteria is revealed for the array structure and control strategy based on the optimal design tradeoff between large array inertia and fast structural dynamics. Moreover, the fundamental limits on strain-actuated solar arrays slew angle magnitude are demonstrated using an intuitive pseudorigid body dynamic model.https://authors.library.caltech.edu/records/6x28v-1p745Probabilistic and Distributed Control of a Large-Scale Swarm of Autonomous Agents
https://resolver.caltech.edu/CaltechAUTHORS:20161122-110230585
Authors: {'items': [{'id': 'Bandyopadhyay-S', 'name': {'family': 'Bandyopadhyay', 'given': 'Saptarshi'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hadaegh-F-Y', 'name': {'family': 'Hadaegh', 'given': 'Fred Y.'}}]}
Year: 2017
DOI: 10.1109/TRO.2017.2705044
We present a distributed control algorithm simultaneously solving both the stochastic target assignment and optimal motion control for large-scale swarms to achieve complex formation shapes. Our probabilistic swarm guidance using inhomogeneous Markov chains (PSG–IMC) algorithm adopts a Eulerian density-control framework, under which the physical space is partitioned into multiple bins and the swarm's density distribution over each bin is controlled in a probabilistic fashion to efficiently handle loss or the addition of agents. We assume that the number of agents is much larger than the number of bins and that each agent knows in which bin it is located, the desired formation shape, and the objective function and motion constraints. PSG–IMC determines the bin-to-bin transition probabilities of each agent using a time IMC. These time-varying Markov matrices are computed by each agent in real time using the feedback from the current swarm distribution, which is estimated in a distributed manner. The PSG–IMC algorithm minimizes the expected cost of transitions per time instant that are required to achieve and maintain the desired formation shape, even if agents are added to or removed from the swarm. PSG–IMC scales well with a large number of agents and complex formation shapes and can also be adapted for area exploration applications. We demonstrate the effectiveness of this proposed swarm guidance algorithm by using numerical simulations and hardware experiments with multiple quadrotors.https://authors.library.caltech.edu/records/5rjx1-p7x91The Visual–Inertial Canoe Dataset
https://resolver.caltech.edu/CaltechAUTHORS:20180706-132152402
Authors: {'items': [{'id': 'Miller-Martin', 'name': {'family': 'Miller', 'given': 'Martin'}, 'orcid': '0000-0003-3520-4946'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hutchinson-Seth', 'name': {'family': 'Hutchinson', 'given': 'Seth'}, 'orcid': '0000-0002-3949-6061'}]}
Year: 2018
DOI: 10.1177/0278364917751842
We present a dataset collected from a canoe along the Sangamon River in Illinois. The canoe was equipped with a stereo camera, an inertial measurement unit (IMU), and a global positioning system (GPS) device, which provide visual data suitable for stereo or monocular applications, inertial measurements, and position data for ground truth. We recorded a canoe trip up and down the river for 44 minutes covering a 2.7 km round trip. The dataset adds to those previously recorded in unstructured environments and is unique in that it is recorded on a river, which provides its own set of challenges and constraints that are described in this paper. The dataset is stored on the Illinois Data Bank and can be accessed at: https://doi.org/10.13012/B2IDB-9342111_V1.https://authors.library.caltech.edu/records/4tq38-7pk10Robust Adaptive Boundary Control of Semilinear PDE Systems Using a Dyadic Controller
https://resolver.caltech.edu/CaltechAUTHORS:20180301-073535766
Authors: {'items': [{'id': 'Paranjape-A-A', 'name': {'family': 'Paranjape', 'given': 'Aditya A.'}, 'orcid': '0000-0002-3164-3215'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2018
DOI: 10.1002/rnc.4075
In this paper, we describe a dyadic adaptive control (DAC) framework for output tracking in a class of semilinear systems of partial differential equations with boundary actuation and unknown distributed nonlinearities. The DAC framework uses the linear terms in the system to split the plant into two virtual sub-systems, one of which contains the nonlinearities, while the other contains the control
input. Full-plant-state feedback is used to estimate the unmeasured, individual states of the two subsystems
as well as the nonlinearities. The control signal is designed to ensure that the controlled sub-system tracks a suitably modified reference signal. We prove well-posedness of the closed-loop system rigorously, and derive conditions for closed-loop stability and robustness using finite-gain L
stability theory.https://authors.library.caltech.edu/records/1bt9e-pfm63Visual-inertial curve simultaneous localization and mapping: Creating a sparse structured world without feature points
https://resolver.caltech.edu/CaltechAUTHORS:20170518-135803615
Authors: {'items': [{'id': 'Meier-K-C', 'name': {'family': 'Meier', 'given': 'Kevin'}, 'orcid': '0000-0003-4000-1422'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hutchinson-Seth', 'name': {'family': 'Hutchinson', 'given': 'Seth'}, 'orcid': '0000-0002-3949-6061'}]}
Year: 2018
DOI: 10.1002/rob.21759
We present a simultaneous localization and mapping (SLAM) algorithm that uses Bézier curves as static landmark primitives rather than feature points. Our approach allows us to estimate the full six degrees of freedom pose of a robot while providing a structured map that can be used to assist a robot in motion planning and control. We demonstrate how to reconstruct the three-dimensional (3D) location of curve landmarks from a stereo pair and how to compare the 3D shape of curve landmarks between chronologically sequential stereo frames to solve the data association problem. We also present a method to combine curve landmarks for mapping purposes, resulting in a map with a continuous set of curves that contain fewer landmark states than conventional point-based SLAM algorithms. We demonstrate our algorithm's effectiveness with numerous experiments, including comparisons to existing state-of-the-art SLAM algorithms.https://authors.library.caltech.edu/records/akvgz-6nh63Robotic Herding of a Flock of Birds Using an Unmanned Aerial Vehicle
https://resolver.caltech.edu/CaltechAUTHORS:20180706-131611070
Authors: {'items': [{'id': 'Paranjape-A-A', 'name': {'family': 'Paranjape', 'given': 'Aditya A.'}, 'orcid': '0000-0002-3164-3215'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Kim-Kyunam', 'name': {'family': 'Kim', 'given': 'Kyunam'}, 'orcid': '0000-0002-7803-1582'}, {'id': 'Shim-D-Hyunchul', 'name': {'family': 'Shim', 'given': 'D. Hyunchul'}, 'orcid': '0000-0002-1929-7022'}]}
Year: 2018
DOI: 10.1109/TRO.2018.2853610
In this paper, we derive an algorithm for enabling a single robotic unmanned aerial vehicle to herd a flock of birds away from a designated volume of space, such as the air space around an airport. The herding algorithm, referred to as the m-waypoint algorithm, is designed using a dynamic model of bird flocking based on Reynolds' rules. We derive bounds on its performance using a combination of reduced-order modeling of the flock's motion, heuristics, and rigorous analysis. A unique contribution of the paper is the experimental demonstration of several facets of the herding algorithm on flocks of live birds reacting to a robotic pursuer. The experiments allow us to estimate several parameters of the flocking model, and especially the interaction between the pursuer and the flock. The herding algorithm is also demonstrated using numerical simulations.https://authors.library.caltech.edu/records/vs2kc-h1056Guest Editorial: Special Section on Aerial Swarm Robotics
https://resolver.caltech.edu/CaltechAUTHORS:20180809-104050194
Authors: {'items': [{'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'S.-J.'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Paranjape-A-A', 'name': {'family': 'Paranjape', 'given': 'A. A.'}, 'orcid': '0000-0002-3164-3215'}, {'id': 'Dames-P', 'name': {'family': 'Dames', 'given': 'P.'}, 'orcid': '0000-0002-7257-0075'}, {'id': 'Shen-Shaojie', 'name': {'family': 'Shen', 'given': 'S.'}, 'orcid': '0000-0002-5573-2909'}, {'id': 'Kumar-Vijay', 'name': {'family': 'Kumar', 'given': 'V.'}, 'orcid': '0000-0002-3902-9391'}]}
Year: 2018
DOI: 10.1109/TRO.2018.2860098
Aerial robotics has been one of the most active areas of research within the robotics community, and recently there have been many reports of promising results in aerial swarm systems. This is partly due to the commoditization of multicopter platforms, and communication, sensing, and processing hardware that has substantially lowered the barriers to entry to the field of aerial swarm robotics.
Aerial swarms differ from swarms of ground-based vehicles in two major respects: Aerial robots or unmanned aerial vehicles (UAVs) operate in a three-dimensional space, and the dynamics of individual vehicles add an extra layer of complexity to the problems of path planning and trajectory design. Furthermore, the success of aerial swarms is predicated on the distributed and synergistic capabilities of individual and cooperative control, estimation, and decision making of aerial robots with limited resources, such as modest onboard computation and sensing capabilities and size, weight, and power constraints.
This special section, starting with the survey paper, presents recent advances in aerial swarm robotics, and aims to put together a cohesive set of research goals and visions toward realizing fully autonomous aerial swarm systems. One objective is to emphasize the three-way tradeoff among computational efficiency for large-scale swarms, stability, and robustness under uncertainty, and the optimal system performance.https://authors.library.caltech.edu/records/zhwx7-yzd26A Survey on Aerial Swarm Robotics
https://resolver.caltech.edu/CaltechAUTHORS:20180717-112943069
Authors: {'items': [{'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Paranjape-A-A', 'name': {'family': 'Paranjape', 'given': 'Aditya'}, 'orcid': '0000-0002-3164-3215'}, {'id': 'Dames-P', 'name': {'family': 'Dames', 'given': 'Philip'}, 'orcid': '0000-0002-7257-0075'}, {'id': 'Shen-Shaojie', 'name': {'family': 'Shen', 'given': 'Shaojie'}, 'orcid': '0000-0002-5573-2909'}, {'id': 'Kumar-Vijay', 'name': {'family': 'Kumar', 'given': 'Vijay'}, 'orcid': '0000-0002-3902-9391'}]}
Year: 2018
DOI: 10.1109/TRO.2018.2857475
The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.https://authors.library.caltech.edu/records/me2vb-qg826Optimizing the structure and movement of a robotic bat with biological kinematic synergies
https://resolver.caltech.edu/CaltechAUTHORS:20180713-074446939
Authors: {'items': [{'id': 'Hoff-Jonathan', 'name': {'family': 'Hoff', 'given': 'Jonathan'}}, {'id': 'Ramezani-Alireza', 'name': {'family': 'Ramezani', 'given': 'Alireza'}, 'orcid': '0000-0002-3391-5288'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hutchinson-Seth', 'name': {'family': 'Hutchinson', 'given': 'Seth'}, 'orcid': '0000-0002-3949-6061'}]}
Year: 2018
DOI: 10.1177/0278364918804654
In this article, we present methods to optimize the design and flight characteristics of a biologically inspired bat-like robot. In previous, work we have designed the topological structure for the wing kinematics of this robot; here we present methods to optimize the geometry of this structure, and to compute actuator trajectories such that its wingbeat pattern closely matches biological counterparts. Our approach is motivated by recent studies on biological bat flight that have shown that the salient aspects of wing motion can be accurately represented in a low-dimensional space. Although bats have over 40 degrees of freedom (DoFs), our robot possesses several biologically meaningful morphing specializations. We use principal component analysis (PCA) to characterize the two most dominant modes of biological bat flight kinematics, and we optimize our robot's parametric kinematics to mimic these. The method yields a robot that is reduced from five degrees of actuation (DoAs) to just three, and that actively folds its wings within a wingbeat period. As a result of mimicking synergies, the robot produces an average net lift improvesment of 89% over the same robot when its wings cannot fold.https://authors.library.caltech.edu/records/kmbjc-rz322Distributed Bayesian Filtering using Logarithmic Opinion Pool for Dynamic Sensor Networks
https://resolver.caltech.edu/CaltechAUTHORS:20180706-132824806
Authors: {'items': [{'id': 'Bandyopadhyay-S', 'name': {'family': 'Bandyopadhyay', 'given': 'Saptarshi'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2018
DOI: 10.1016/j.automatica.2018.07.013
The discrete-time Distributed Bayesian Filtering (DBF) algorithm is presented for the problem of tracking a target dynamic model using a time-varying network of heterogeneous sensing agents. In the DBF algorithm, the sensing agents combine their normalized likelihood functions in a distributed manner using the logarithmic opinion pool and the dynamic average consensus algorithm. We show that each agent's estimated likelihood function globally exponentially converges to an error ball centered on the joint likelihood function of the centralized multi-sensor Bayesian filtering algorithm. We rigorously characterize the convergence, stability, and robustness properties of the DBF algorithm. Moreover, we provide an explicit bound on the time step size of the DBF algorithm that depends on the time-scale of the target dynamics, the desired convergence error bound, and the modeling and communication error bounds. Furthermore, the DBF algorithm for linear-Gaussian models is cast into a modified form of the Kalman information filter. The performance and robust properties of the DBF algorithm are validated using numerical simulations.https://authors.library.caltech.edu/records/k6cst-2hd35Nonlinear Attitude Control of a Spacecraft with Distributed Actuation of Solar Arrays
https://resolver.caltech.edu/CaltechAUTHORS:20180706-131141775
Authors: {'items': [{'id': 'Nakka-Yashwanth-K', 'name': {'family': 'Nakka', 'given': 'Yashwanth Kumar'}, 'orcid': '0000-0001-7897-3644'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Allison-James-T', 'name': {'family': 'Allison', 'given': 'James T.'}}, {'id': 'Aldrich-Jack-B', 'name': {'family': 'Aldrich', 'given': 'Jack B.'}}, {'id': 'Alvarez-Salazar-O-S', 'name': {'family': 'Alvarez-Salazar', 'given': 'Oscar S.'}}]}
Year: 2019
DOI: 10.2514/1.G003478
This paper presents a novel control architecture and algorithm for precision attitude control of a one-degree-of-freedom dynamic model of a spacecraft. To achieve a parametric model-based control design approach for this new spacecraft actuation and control architecture, the nonlinear dynamics of the open-loop plant are modeled as an ordinary differential equation (ODE)–partial differential equation (PDE) system. The ODE describes the spacecraft single-axis rigid-body rotation, and the PDE describes the spatially continuous flexible dynamics of the solar array including an allocation for a multi-input distributed piezoelectric actuation system bonded on the solar array. This distributed actuation system is called strain-actuated solar arrays. Based on this plant model, a nonlinear ODE–PDE feedback controller for attitude trajectory tracking and slewing is presented with detailed stability proofs. From an end-to-end point of view, the controller drives the distributed piezoelectric actuator patches with voltages that induce bending deflections in the solar arrays, causing controlled reaction torques on the bus to yield target-following motions and precision spacecraft attitude control. The proposed algorithm can be extended to any distributed actuation system with appropriate control input to actuator input mapping. The benefits and limitations of the proposed attitude control method using strain actuation are discussed later in terms of solar array inertia and structural rigidity. This paper also reports experimental results that demonstrate command-following rotations of a cylindrical bus via closed-loop control of its flexible appendages.https://authors.library.caltech.edu/records/1wyk9-yjm56Monocular-Based Pose Determination of Uncooperative Space Objects
https://resolver.caltech.edu/CaltechAUTHORS:20191001-104800482
Authors: {'items': [{'id': 'Capuano-V', 'name': {'family': 'Capuano', 'given': 'Vincenzo'}, 'orcid': '0000-0002-6886-5719'}, {'id': 'Kim-Kyunam', 'name': {'family': 'Kim', 'given': 'Kyunam'}, 'orcid': '0000-0002-7803-1582'}, {'id': 'Harvard-A', 'name': {'family': 'Harvard', 'given': 'Alexei'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2020
DOI: 10.1016/j.actaastro.2019.09.027
Vision-based methods to determine the relative pose of an uncooperative orbiting object are investigated in applications to spacecraft proximity operations, such as on-orbit servicing, spacecraft formation flying, and small bodies exploration. Depending on whether the object is known or unknown, a shape model of the orbiting target object may have to be constructed autonomously in real-time by making use of only optical measurements. The Simultaneous Estimation of Pose and Shape (SEPS) algorithm that does not require a priori knowledge of the pose and shape of the target is presented. This makes use of a novel measurement equation and filter that can efficiently use optical flow information along with a star tracker to estimate the target's angular rotational and translational relative velocity as well as its center of gravity. Depending on the mission constraints, SEPS can be augmented by a more accurate offline, on-board 3D reconstruction of the target shape, which allows for the estimation of the pose as a known target. The use of Structure from Motion (SfM) for this purpose is discussed. A model-based approach for pose estimation of known targets is also presented. The architecture and implementation of both the proposed approaches are elucidated and their performance metrics are evaluated through numerical simulations by using a dataset of images that are synthetically generated according to a chaser/target relative motion in Geosynchronous Orbit (GEO).https://authors.library.caltech.edu/records/bxxzy-w9k62Optimal Guidance and Control with Nonlinear Dynamics Using Sequential Convex Programming
https://resolver.caltech.edu/CaltechAUTHORS:20191029-160904084
Authors: {'items': [{'id': 'Foust-Rebecca', 'name': {'family': 'Foust', 'given': 'Rebecca'}, 'orcid': '0000-0003-1470-1716'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hadaegh-F-Y', 'name': {'family': 'Hadaegh', 'given': 'Fred Y.'}}]}
Year: 2020
DOI: 10.2514/1.G004590
This paper presents a novel method for expanding the use of sequential convex programming (SCP) to the domain of optimal guidance and control problems with nonlinear dynamics constraints. SCP is a useful tool in obtaining real-time solutions to direct optimal control, but it is unable to adequately model nonlinear dynamics due to the linearization and discretization required. As nonlinear program solvers are not yet functioning in real-time, a tool is needed to bridge the gap between satisfying the nonlinear dynamics and completing execution fast enough to be useful. Two methods are proposed, sequential convex programming with nonlinear dynamics correction (SCPn) and modified SCPn (M-SCPn), which mixes SCP and SCPn to reduce runtime and improve algorithmic robustness. Both methods are proven to generate optimal state and control trajectories that satisfy the nonlinear dynamics. Simulations are presented to validate the efficacy of the methods as compared to SCP.https://authors.library.caltech.edu/records/x3eq7-xwb80Autonomous In-Orbit Satellite Assembly from a Modular Heterogeneous Swarm
https://resolver.caltech.edu/CaltechAUTHORS:20200107-111109197
Authors: {'items': [{'id': 'Foust-Rebecca', 'name': {'family': 'Foust', 'given': 'Rebecca C.'}, 'orcid': '0000-0003-1470-1716'}, {'id': 'Lupu-E-S', 'name': {'family': 'Lupu', 'given': 'E. Sorina'}, 'orcid': '0000-0002-3968-2630'}, {'id': 'Nakka-Yashwanth-K', 'name': {'family': 'Nakka', 'given': 'Yashwanth K.'}, 'orcid': '0000-0001-7897-3644'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hadaegh-F-Y', 'name': {'family': 'Hadaegh', 'given': 'Fred Y.'}}]}
Year: 2020
DOI: 10.1016/j.actaastro.2020.01.006
This paper presents a decentralized, distributed guidance and control scheme to combine a heterogeneous swarm of component satellites into a large satellite structure. The component satellites for the heterogeneous swarm are chosen to promote flexibility in final shape inspired by crystal structures and Islamic tile art. After the ideal fundamental building blocks are selected, basic nanosatellite-class satellite designs are made to assist in simulations involving attitude control. The Swarm Orbital Construction Algorithm (SOCA) is a guidance and control algorithm to allow for the limited type heterogeneity and docking ability required for in-orbit assembly. The algorithm consists of two parts, a distributed auction which uses barrier functions to ensure the proper agent selection for each target, and a trajectory generation portion which leverages model predictive control and sequential convex programming to achieve optimal collision-free trajectories to the desired target point even with nonlinear system dynamics. The optimization constraints use a boundary layer to determine whether the collision avoidance or the docking constraints should be applied. The algorithm was tested in a simulated perturbed 6-DOF spacecraft dynamic environment for planar and out-of-plane final structures and on two robotic platforms, including a swarm of frictionless spacecraft simulation robots.https://authors.library.caltech.edu/records/xe3ng-16k57GLAS: Global-to-Local Safe Autonomy Synthesis for Multi-Robot Motion Planning with End-to-End Learning
https://resolver.caltech.edu/CaltechAUTHORS:20200514-141356088
Authors: {'items': [{'id': 'Rivière-B', 'name': {'family': 'Rivière', 'given': 'Benjamin'}, 'orcid': '0000-0003-4189-4090'}, {'id': 'Hönig-W', 'name': {'family': 'Hönig', 'given': 'Wolfgang'}, 'orcid': '0000-0002-0773-028X'}, {'id': 'Yue-Yisong', 'name': {'family': 'Yue', 'given': 'Yisong'}, 'orcid': '0000-0001-9127-1989'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2020
DOI: 10.1109/lra.2020.2994035
We present GLAS: Global-to- Local Autonomy Synthesis, a provably-safe, automated distributed policy generation for multi-robot motion planning. Our approach combines the advantage of centralized planning of avoiding local minima with the advantage of decentralized controllers of scalability and distributed computation. In particular, our synthesized policies only require relative state information of nearby neighbors and obstacles, and compute a provably-safe action. Our approach has three major components: i) we generate demonstration trajectories using a global planner and extract local observations from them, ii) we use deep imitation learning to learn a decentralized policy that can run efficiently online, and iii) we introduce a novel differentiable safety module to ensure collision-free operation, thereby allowing for end-to-end policy training. Our numerical experiments demonstrate that our policies have a 20% higher success rate than optimal reciprocal collision avoidance, ORCA, across a wide range of robot and obstacle densities. We demonstrate our method on an aerial swarm, executing the policy on low-end microcontrollers in real-time.https://authors.library.caltech.edu/records/wg2w8-e6v42Contraction theory for nonlinear stability analysis and learning-based control: A tutorial overview
https://resolver.caltech.edu/CaltechAUTHORS:20220308-454020000
Authors: {'items': [{'id': 'Tsukamoto-Hiroyasu', 'name': {'family': 'Tsukamoto', 'given': 'Hiroyasu'}, 'orcid': '0000-0002-6337-2667'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Slotine-Jean-Jac', 'name': {'family': 'Slotine', 'given': 'Jean-Jaques E.'}, 'orcid': '0000-0002-7161-7812'}]}
Year: 2021
DOI: 10.1016/j.arcontrol.2021.10.001
Contraction theory is an analytical tool to study differential dynamics of a non-autonomous (i.e., time-varying) nonlinear system under a contraction metric defined with a uniformly positive definite matrix, the existence of which results in a necessary and sufficient characterization of incremental exponential stability of multiple solution trajectories with respect to each other. By using a squared differential length as a Lyapunov-like function, its nonlinear stability analysis boils down to finding a suitable contraction metric that satisfies a stability condition expressed as a linear matrix inequality, indicating that many parallels can be drawn between well-known linear systems theory and contraction theory for nonlinear systems. Furthermore, contraction theory takes advantage of a superior robustness property of exponential stability used in conjunction with the comparison lemma. This yields much-needed safety and stability guarantees for neural network-based control and estimation schemes, without resorting to a more involved method of using uniform asymptotic stability for input-to-state stability. Such distinctive features permit systematic construction of a contraction metric via convex optimization, thereby obtaining an explicit exponential bound on the distance between a time-varying target trajectory and solution trajectories perturbed externally due to disturbances and learning errors. The objective of this paper is therefore to present a tutorial overview of contraction theory and its advantages in nonlinear stability analysis of deterministic and stochastic systems, with an emphasis on deriving formal robustness and stability guarantees for various learning-based and data-driven automatic control methods. In particular, we provide a detailed review of techniques for finding contraction metrics and associated control and estimation laws using deep neural networks.https://authors.library.caltech.edu/records/0fpzv-23s25Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach
https://resolver.caltech.edu/CaltechAUTHORS:20200624-155134352
Authors: {'items': [{'id': 'Tsukamoto-Hiroyasu', 'name': {'family': 'Tsukamoto', 'given': 'Hiroyasu'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2021
DOI: 10.1109/lcsys.2020.3001646
This letter presents a new deep learning-based framework for robust nonlinear estimation and control using the concept of a Neural Contraction Metric (NCM). The NCM uses a deep long short-term memory recurrent neural network for a global approximation of an optimal contraction metric, the existence of which is a necessary and sufficient condition for exponential stability of nonlinear systems. The optimality stems from the fact that the contraction metrics sampled offline are the solutions of a convex optimization problem to minimize an upper bound of the steady-state Euclidean distance between perturbed and unperturbed system trajectories. We demonstrate how to exploit NCMs to design an online optimal estimator and controller for nonlinear systems with bounded disturbances utilizing their duality. The performance of our framework is illustrated through Lorenz oscillator state estimation and spacecraft optimal motion planning problems.https://authors.library.caltech.edu/records/zmec9-3ky05River segmentation for autonomous surface vehicle localization and river boundary mapping
https://resolver.caltech.edu/CaltechAUTHORS:20200929-090809842
Authors: {'items': [{'id': 'Meier-Kevin-C', 'name': {'family': 'Meier', 'given': 'Kevin'}, 'orcid': '0000-0003-4000-1422'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hutchinson-Seth', 'name': {'family': 'Hutchinson', 'given': 'Seth'}, 'orcid': '0000-0002-3949-6061'}]}
Year: 2021
DOI: 10.1002/rob.21989
We present a vision‐based algorithm that identifies the boundary separating water from land in a river environment containing specular reflections. Our approach relies on the law of reflection. Assuming the surface of water behaves like a horizontal mirror, the border separating land from water corresponds to the border separating three‐dimensional (3D) data which are either above or below the surface of water. We detect a river by identifying this border in a stereo camera. We start by demonstrating how to robustly estimate the normal and height of the water's surface with respect to a stereo camera. Then, we segment water from land by identifying the boundary separating dense 3D stereo data which are either above or below the water's surface. We explicitly show how to find this boundary by formulating and solving a graph‐based optimization problem using dense 3D stereo data near the shoreline and Dijkstra's algorithm. With the border of water identified, we validate the proposed river boundary detection algorithm by applying it to a chronologically sequential video sequence obtained from the visual‐inertial canoe data set. The intended purpose of the proposed river segmentation algorithm is to be used as a front‐end object recognition module for solving the simultaneous localization and mapping (SLAM) problem; therefore, using the extracted river boundary, we apply the recently developed visual‐inertial Curve SLAM algorithm to localize a canoe and create a sparse map that recovers the outline, shape, and dimensions of the shoreline of a river.https://authors.library.caltech.edu/records/bekqy-2bn19Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems
https://resolver.caltech.edu/CaltechAUTHORS:20200526-150616242
Authors: {'items': [{'id': 'Nakka-Yashwanth-K', 'name': {'family': 'Nakka', 'given': 'Yashwanth Kumar'}, 'orcid': '0000-0001-7897-3644'}, {'id': 'Liu-Anqi', 'name': {'family': 'Liu', 'given': 'Anqi'}}, {'id': 'Shi-Guanya', 'name': {'family': 'Shi', 'given': 'Guanya'}, 'orcid': '0000-0002-9075-3705'}, {'id': 'Anandkumar-A', 'name': {'family': 'Anandkumar', 'given': 'Anima'}}, {'id': 'Yue-Yisong', 'name': {'family': 'Yue', 'given': 'Yisong'}, 'orcid': '0000-0001-9127-1989'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2021
DOI: 10.1109/LRA.2020.3044033
Learning-based control algorithms require data collection with abundant supervision for training. Safe exploration algorithms ensure the safety of this data collection process even when only partial knowledge is available. We present a new approach for optimal motion planning with safe exploration that integrates chance-constrained stochastic optimal control with dynamics learning and feedback control. We derive an iterative convex optimization algorithm that solves an Information-cost Stochastic Nonlinear Optimal Control problem (Info-SNOC). The optimization objective encodes control cost for performance and exploration cost for learning, and the safety is incorporated as distributionally robust chance constraints. The dynamics are predicted from a robust regression model that is learned from data. The Info-SNOC algorithm is used to compute a sub-optimal pool of safe motion plans that aid in exploration for learning unknown residual dynamics under safety constraints. A stable feedback controller is used to execute the motion plan and collect data for model learning. We prove the safety of rollout from our exploration method and reduction in uncertainty over epochs, thereby guaranteeing the consistency of our learning method. We validate the effectiveness of Info-SNOC by designing and implementing a pool of safe trajectories for a planar robot. We demonstrate that our approach has higher success rate in ensuring safety when compared to a deterministic trajectory optimization approach.https://authors.library.caltech.edu/records/m40dq-4s262Decentralized formation pose estimation for spacecraft swarms
https://resolver.caltech.edu/CaltechAUTHORS:20200610-090345377
Authors: {'items': [{'id': 'Matsuka-Kai', 'name': {'family': 'Matsuka', 'given': 'Kai'}, 'orcid': '0000-0003-2116-9756'}, {'id': 'Feldman-Aaron-O', 'name': {'family': 'Feldman', 'given': 'Aaron O.'}}, {'id': 'Lupu-Elena-S', 'name': {'family': 'Lupu', 'given': 'Elena S.'}, 'orcid': '0000-0002-3968-2630'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Hadaegh-Fred-Y', 'name': {'family': 'Hadaegh', 'given': 'Fred Y.'}}]}
Year: 2021
DOI: 10.1016/j.asr.2020.06.016
For spacecraft swarms, the multi-agent localization algorithm must scale well with the number of spacecraft and adapt to time-varying communication and relative sensing networks. In this paper, we present a decentralized, scalable algorithm for swarm localization, called the Decentralized Pose Estimation (DPE) algorithm. The DPE considers both communication and relative sensing graphs and defines an observable local formation. Each spacecraft jointly localizes its local subset of spacecraft using direct and communicated measurements. Since the algorithm is local, the algorithm complexity does not grow with the number of spacecraft in the swarm. As part of the DPE, we present the Swarm Reference Frame Estimation (SRFE) algorithm, a distributed consensus algorithm to co-estimate a common Local-Vertical, Local-Horizontal (LVLH) frame. The DPE combined with the SRFE provides a scalable, fully-decentralized navigation solution that can be used for swarm control and motion planning. Numerical simulations and experiments using Caltech's robotic spacecraft simulators are presented to validate the effectiveness and scalability of the DPE algorithm.https://authors.library.caltech.edu/records/5zy30-t4193A seasonally invariant deep transform for visual terrain-relative navigation
https://resolver.caltech.edu/CaltechAUTHORS:20210624-195102852
Authors: {'items': [{'id': 'Fragoso-Anthony-T', 'name': {'family': 'Fragoso', 'given': 'Anthony T.'}, 'orcid': '0000-0002-5805-9668'}, {'id': 'Lee-Connor-T', 'name': {'family': 'Lee', 'given': 'Connor T.'}, 'orcid': '0000-0002-5008-4092'}, {'id': 'McCoy-Austin-S', 'name': {'family': 'McCoy', 'given': 'Austin S.'}, 'orcid': '0000-0003-3777-4475'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2021
DOI: 10.1126/scirobotics.abf3320
Visual terrain-relative navigation (VTRN) is a localization method based on registering a source image taken from a robotic vehicle against a georeferenced target image. With high-resolution imagery databases of Earth and other planets now available, VTRN offers accurate, drift-free navigation for air and space robots even in the absence of external positioning signals. Despite its potential for high accuracy, however, VTRN remains extremely fragile to common and predictable seasonal effects, such as lighting, vegetation changes, and snow cover. Engineered registration algorithms are mature and have provable geometric advantages but cannot accommodate the content changes caused by seasonal effects and have poor matching skill. Approaches based on deep learning can accommodate image content changes but produce opaque position estimates that either lack an interpretable uncertainty or require tedious human annotation. In this work, we address these issues with targeted use of deep learning within an image transform architecture, which converts seasonal imagery to a stable, invariant domain that can be used by conventional algorithms without modification. Our transform preserves the geometric structure and uncertainty estimates of legacy approaches and demonstrates superior performance under extreme seasonal changes while also being easy to train and highly generalizable. We show that classical registration methods perform exceptionally well for robotic visual navigation when stabilized with the proposed architecture and are able to consistently anticipate reliable imagery. Gross mismatches were nearly eliminated in challenging and realistic visual navigation tasks that also included topographic and perspective effects.https://authors.library.caltech.edu/records/8brhn-19h41Robust Controller Design for Stochastic Nonlinear Systems via Convex Optimization
https://resolver.caltech.edu/CaltechAUTHORS:20191029-154243537
Authors: {'items': [{'id': 'Tsukamoto-Hiroyasu', 'name': {'family': 'Tsukamoto', 'given': 'Hiroyasu'}, 'orcid': '0000-0002-6337-2667'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2021
DOI: 10.1109/TAC.2020.3038402
This article presents ConVex optimization-based Stochastic steady-state Tracking Error Minimization (CV-STEM), a new state feedback control framework for a class of Itô stochastic nonlinear systems and Lagrangian systems. Its innovation lies in computing the control input by an optimal contraction metric, which greedily minimizes an upper bound of the steady-state mean squared tracking error of the system trajectories. Although the problem of minimizing the bound is nonconvex, its equivalent convex formulation is proposed utilizing SDC parameterizations of the nonlinear system equation. It is shown using stochastic incremental contraction analysis that the CV-STEM provides a sufficient guarantee for exponential boundedness of the error for all time with L₂-robustness properties. For the sake of its sampling-based implementation, we present discrete-time stochastic contraction analysis with respect to a state- and time-dependent metric along with its explicit connection to continuous-time cases. We validate the superiority of the CV-STEM to PID, H∞, and baseline nonlinear controllers for spacecraft attitude control and synchronization problems.https://authors.library.caltech.edu/records/38h8h-egc30Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments
https://resolver.caltech.edu/CaltechAUTHORS:20210510-141334067
Authors: {'items': [{'id': 'Rivière-Benjamin', 'name': {'family': 'Rivière', 'given': 'Benjamin'}, 'orcid': '0000-0003-4189-4090'}, {'id': 'Hoenig-Wolfgang', 'name': {'family': 'Hoenig', 'given': 'Wolfgang'}, 'orcid': '0000-0002-0773-028X'}, {'id': 'Anderson-Matthew-J', 'name': {'family': 'Anderson', 'given': 'Matthew'}, 'orcid': '0000-0001-8884-3448'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2021
DOI: 10.1109/LRA.2021.3096758
We present a self-improving, Neural Tree Expansion (NTE) method for multi-robot online planning in non-cooperative environments, where each robot attempts to maximize its cumulative reward while interacting with other self-interested robots. Our algorithm adapts the centralized, perfect information, discrete-action space method from AlphaZero to a decentralized, partial information, continuous action space setting for multi-robot applications. Our method has three interacting components: (i) a centralized, perfect-information "expert" Monte Carlo Tree Search (MCTS) with large computation resources that provides expert demonstrations, (ii) a decentralized, partial-information "learner" MCTS with small computation resources that runs in real-time and provides self-play examples, and (iii) policy & value neural networks that are trained with the expert demonstrations and bias both the expert and the learner tree growth. Our numerical experiments demonstrate Neural Tree Expansion's computational advantage by finding better solutions than a MCTS with 20 times more resources. The resulting policies are dynamically sophisticated, demonstrate coordination between robots, and play the Reach-Target-Avoid differential game significantly better than the state-of-the-art control-theoretic baseline for multi-robot, double-integrator systems. Our hardware experiments on an aerial swarm demonstrate the computational advantage of Neural Tree Expansion, enabling online planning at 20 Hz with effective policies in complex scenarios.https://authors.library.caltech.edu/records/1khj9-scp36Learning-based Robust Motion Planning With Guaranteed Stability: A Contraction Theory Approach
https://resolver.caltech.edu/CaltechAUTHORS:20210304-094303690
Authors: {'items': [{'id': 'Tsukamoto-Hiroyasu', 'name': {'family': 'Tsukamoto', 'given': 'Hiroyasu'}, 'orcid': '0000-0002-6337-2667'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2021
DOI: 10.1109/LRA.2021.3091019
This letter presents Learning-based Autonomous Guidance with RObustness and Stability guarantees (LAG-ROS), which provides machine learning-based nonlinear motion planners with formal robustness and stability guarantees, by designing a differential Lyapunov function using contraction theory. LAG-ROS utilizes a neural network to model a robust tracking controller independently of a target trajectory, for which we show that the Euclidean distance between the target and controlled trajectories is exponentially bounded linearly in the learning error, even under the existence of bounded external disturbances. We also present a convex optimization approach that minimizes the steady-state bound of the tracking error to construct the robust control law for neural network training. In numerical simulations, it is demonstrated that the proposed method indeed possesses superior properties of robustness and nonlinear stability resulting from contraction theory, whilst retaining the computational efficiency of existing learning-based motion planners.https://authors.library.caltech.edu/records/qkadh-4v651A bipedal walking robot that can fly, slackline, and skateboard
https://resolver.caltech.edu/CaltechAUTHORS:20211007-153559085
Authors: {'items': [{'id': 'Kim-Kyunam', 'name': {'family': 'Kim', 'given': 'Kyunam'}, 'orcid': '0000-0002-7803-1582'}, {'id': 'Spieler-Patrick', 'name': {'family': 'Spieler', 'given': 'Patrick'}}, {'id': 'Lupu-Elena-Sorina', 'name': {'family': 'Lupu', 'given': 'Elena Sorina'}, 'orcid': '0000-0002-3968-2630'}, {'id': 'Ramezani-Alireza', 'name': {'family': 'Ramezani', 'given': 'Alireza'}, 'orcid': '0000-0002-3391-5288'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2021
DOI: 10.1126/scirobotics.abf8136
Numerous mobile robots in various forms specialize in either ground or aerial locomotion, whereas very few robots can perform complex locomotion tasks beyond simple walking and flying. We present the design and control of a multimodal locomotion robotic platform called LEONARDO, which bridges the gap between two different locomotion regimes of flying and walking using synchronized control of distributed electric thrusters and a pair of multijoint legs. By combining two distinct locomotion mechanisms, LEONARDO achieves complex maneuvers that require delicate balancing, such as walking on a slackline and skateboarding, which are challenging for existing bipedal robots. LEONARDO also demonstrates agile walking motions, interlaced with flying maneuvers to overcome obstacles using synchronized control of propellers and leg joints. The mechanical design and synchronized control strategy achieve a unique multimodal locomotion capability that could potentially enable robotic missions and operations that would be difficult for single-modal locomotion robots.https://authors.library.caltech.edu/records/a3y6x-12v73Neural Stochastic Contraction Metrics for Learning-based Control and Estimation
https://resolver.caltech.edu/CaltechAUTHORS:20210113-163505450
Authors: {'items': [{'id': 'Tsukamoto-Hiroyasu', 'name': {'family': 'Tsukamoto', 'given': 'Hiroyasu'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Slotine-Jean-Jacques-E', 'name': {'family': 'Slotine', 'given': 'Jean-Jacques E.'}, 'orcid': '0000-0002-7161-7812'}]}
Year: 2021
DOI: 10.1109/lcsys.2020.3046529
We present Neural Stochastic Contraction Metrics (NSCM), a new design framework for provably-stable learning-based control and estimation for a class of stochastic nonlinear systems. It uses a spectrally-normalized deep neural network to construct a contraction metric and its differential Lyapunov function, sampled via simplified convex optimization in the stochastic setting. Spectral normalization constrains the state-derivatives of the metric to be Lipschitz continuous, thereby ensuring exponential boundedness of the mean squared distance of system trajectories under stochastic disturbances. The trained NSCM model allows autonomous systems to approximate optimal stable control and estimation policies in real-time, and outperforms existing nonlinear control and estimation techniques including the state-dependent Riccati equation, iterative LQR, EKF, and the deterministic NCM, as shown in simulation results.https://authors.library.caltech.edu/records/9b7jr-g4j06H-TD²: Hybrid Temporal Difference Learning for Adaptive Urban Taxi Dispatch
https://resolver.caltech.edu/CaltechAUTHORS:20220120-890613000
Authors: {'items': [{'id': 'Rivière-Benjamin', 'name': {'family': 'Rivière', 'given': 'Benjamin'}, 'orcid': '0000-0003-4189-4090'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2022
DOI: 10.1109/tits.2021.3097297
We present H-TD²: Hybrid Temporal Difference Learning for Taxi Dispatch, a model-free, adaptive decision-making algorithm to coordinate a large fleet of automated taxis in a dynamic urban environment to minimize expected customer waiting times. Our scalable algorithm exploits the natural transportation network company topology by switching between two behaviors: distributed temporal-difference learning computed locally at each taxi and infrequent centralized Bellman updates computed at the dispatch center. We derive a regret bound and design the trigger condition between the two behaviors to explicitly control the trade-off between computational complexity and the individual taxi policy's bounded sub-optimality; this advances the state of the art by enabling distributed operation with bounded-suboptimality. Additionally, unlike recent reinforcement learning dispatch methods, this policy estimation is adaptive and robust to out-of-training domain events. This result is enabled by a two-step modelling approach: the policy is learned on an agent-agnostic, cell-based Markov Decision Process and individual taxis are coordinated using the learned policy in a distributed game-theoretic task assignment. We validate our algorithm against a receding horizon control baseline in a Gridworld environment with a simulated customer dataset, where the proposed solution decreases average customer waiting time by 50% over a wide range of parameters. We also validate in a Chicago city environment with real customer requests from the Chicago taxi public dataset where the proposed solution decreases average customer waiting time by 26% over irregular customer distributions during a 2016 Major League Baseball World Series game.https://authors.library.caltech.edu/records/rmv3t-7zv14Neural-Swarm2: Planning and Control of Heterogeneous Multirotor Swarms Using Learned Interactions
https://resolver.caltech.edu/CaltechAUTHORS:20210120-165259145
Authors: {'items': [{'id': 'Shi-Guanya', 'name': {'family': 'Shi', 'given': 'Guanya'}, 'orcid': '0000-0002-9075-3705'}, {'id': 'Hönig-Wolfgang', 'name': {'family': 'Hönig', 'given': 'Wolfgang'}, 'orcid': '0000-0002-0773-028X'}, {'id': 'Shi-Xichen', 'name': {'family': 'Shi', 'given': 'Xichen'}, 'orcid': '0000-0002-5366-9256'}, {'id': 'Yue-Yisong', 'name': {'family': 'Yue', 'given': 'Yisong'}, 'orcid': '0000-0001-9127-1989'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2022
DOI: 10.1109/TRO.2021.3098436
We present Neural-Swarm2 , a learning-based method for motion planning and control that allows heterogeneous multirotors in a swarm to safely fly in close proximity. Such operation for drones is challenging due to complex aerodynamic interaction forces, such as downwash generated by nearby drones and ground effect. Conventional planning and control methods neglect capturing these interaction forces, resulting in sparse swarm configuration during flight. Our approach combines a physics-based nominal dynamics model with learned deep neural networks with strong Lipschitz properties. We make use of two techniques to accurately predict the aerodynamic interactions between heterogeneous multirotors: 1) Spectral normalization for stability and generalization guarantees of unseen data and 2) heterogeneous deep sets for supporting any number of heterogeneous neighbors in a permutation-invariant manner without reducing expressiveness. The learned residual dynamics benefit both the proposed interaction-aware multirobot motion planning and the nonlinear tracking control design because the learned interaction forces reduce the modelling errors. Experimental results demonstrate that Neural-Swarm2 is able to generalize to larger swarms beyond training cases and significantly outperforms a baseline nonlinear tracking controller with up to three times reduction in worst-case tracking errors.https://authors.library.caltech.edu/records/psxc9-vps89Neural-Fly enables rapid learning for agile flight in strong winds
https://resolver.caltech.edu/CaltechAUTHORS:20220505-792409800
Authors: {'items': [{'id': "O'Connell-Michael", 'name': {'family': "O'Connell", 'given': 'Michael'}, 'orcid': '0000-0001-6681-8823'}, {'id': 'Shi-Guanya', 'name': {'family': 'Shi', 'given': 'Guanya'}, 'orcid': '0000-0002-9075-3705'}, {'id': 'Shi-Xichen', 'name': {'family': 'Shi', 'given': 'Xichen'}, 'orcid': '0000-0002-5366-9256'}, {'id': 'Azizzadenesheli-Kamyar', 'name': {'family': 'Azizzadenesheli', 'given': 'Kamyar'}, 'orcid': '0000-0001-8507-1868'}, {'id': 'Anandkumar-A', 'name': {'family': 'Anandkumar', 'given': 'Anima'}, 'orcid': '0000-0002-6974-6797'}, {'id': 'Yue-Yisong', 'name': {'family': 'Yue', 'given': 'Yisong'}, 'orcid': '0000-0001-9127-1989'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2022
DOI: 10.1126/scirobotics.abm6597
Executing safe and precise flight maneuvers in dynamic high-speed winds is important for the ongoing commoditization of uninhabited aerial vehicles (UAVs). However, because the relationship between various wind conditions and its effect on aircraft maneuverability is not well understood, it is challenging to design effective robot controllers using traditional control design methods. We present Neural-Fly, a learning-based approach that allows rapid online adaptation by incorporating pretrained representations through deep learning. Neural-Fly builds on two key observations that aerodynamics in different wind conditions share a common representation and that the wind-specific part lies in a low-dimensional space. To that end, Neural-Fly uses a proposed learning algorithm, domain adversarially invariant meta-learning (DAIML), to learn the shared representation, only using 12 minutes of flight data. With the learned representation as a basis, Neural-Fly then uses a composite adaptation law to update a set of linear coefficients for mixing the basis elements. When evaluated under challenging wind conditions generated with the Caltech Real Weather Wind Tunnel, with wind speeds up to 43.6 kilometers/hour (12.1 meters/second), Neural-Fly achieves precise flight control with substantially smaller tracking error than stateof-the-art nonlinear and adaptive controllers. In addition to strong empirical performance, the exponential stability of Neural-Fly results in robustness guarantees. Last, our control design extrapolates to unseen wind conditions, is shown to be effective for outdoor flights with only onboard sensors, and can transfer across drones with minimal performance degradation.https://authors.library.caltech.edu/records/q3grb-3vz72Information-Based Guidance and Control Architecture for Multi-Spacecraft On-Orbit Inspection
https://resolver.caltech.edu/CaltechAUTHORS:20220517-496840000
Authors: {'items': [{'id': 'Nakka-Yashwanth-Kumar-K', 'name': {'family': 'Nakka', 'given': 'Yashwanth Kumar K.'}, 'orcid': '0000-0001-7897-3644'}, {'id': 'Hönig-Wolfgang', 'name': {'family': 'Hönig', 'given': 'Wolfgang'}, 'orcid': '0000-0002-0773-028X'}, {'id': 'Choi-Changrak', 'name': {'family': 'Choi', 'given': 'Changrak'}}, {'id': 'Harvard-Alexei', 'name': {'family': 'Harvard', 'given': 'Alexei'}}, {'id': 'Rahmani-Amir', 'name': {'family': 'Rahmani', 'given': 'Amir'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2022
DOI: 10.2514/1.g006278
Inspection or mapping of a target spacecraft in a low Earth orbit using multiple observer spacecraft in stable passive relative orbits (PROs) is a key enabling technology for future space missions. Our guidance and control architecture uses an information gain approach to directly consider the tradeoff between gathered data and fuel/energy cost. The architecture has four components: information estimation, spacecraft's absolute and relative state estimation, motion planning for relative orbit initialization and reconfiguration, and relative orbit control. The information estimation quantifies the information gain during inspection of a spacecraft, given past and potential future poses of all spacecraft. The estimated information gain is a crucial input to the motion planner, which computes PROs and reconfiguration strategies for each observer to maximize the information gain from distributed observations of the target spacecraft. The resulting motion trajectories jointly consider observational coverage of the target spacecraft and fuel/energy cost. For the PRO trajectories, a fuel-optimal attitude trajectory that minimizes rest-to-rest energy for each observer to inspect the target spacecraft is designed. The validation on a mission simulation to visually inspect the target spacecraft and on a three-degree-of-freedom robotic spacecraft dynamics simulator testbed demonstrates the effectiveness and versatility of our approach.https://authors.library.caltech.edu/records/g57e8-z8965Incremental nonlinear stability analysis of stochastic systems perturbed by Lévy noise
https://resolver.caltech.edu/CaltechAUTHORS:20210510-141340828
Authors: {'items': [{'id': 'Han-SooJean', 'name': {'family': 'Han', 'given': 'SooJean'}}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2022
DOI: 10.1002/rnc.6216
We present a theoretical framework for characterizing incremental stability of nonlinear stochastic systems perturbed by either compound Poisson shot noise or finite-measure Lévy noise. For each noise type, we compare trajectories of the perturbed system with distinct noise sample paths against trajectories of the nominal, unperturbed system. We show that for a finite number of jumps arising from the noise process, the mean-squared error between the trajectories exponentially converge toward a bounded error ball across a finite interval of time under practical boundedness assumptions. The convergence rate for shot noise systems is the same as the exponentially stable nominal system, but with a tradeoff between the parameters of the shot noise process and the size of the error ball. The convergence rate and the error ball for the Lévy noise system are shown to be nearly direct sums of the respective quantities for the shot and white noise systems separately, a result which is analogous to the Lévy–Khintchine theorem. We demonstrate both empirical and analytical computation of the error ball using several numerical examples, and illustrate how varying the parameters of the system affect the tightness of the bound.https://authors.library.caltech.edu/records/8p58p-eag56Trajectory Optimization of Chance-Constrained Nonlinear Stochastic Systems for Motion Planning Under Uncertainty
https://resolver.caltech.edu/CaltechAUTHORS:20220908-230525012
Authors: {'items': [{'id': 'Nakka-Yashwanth-K', 'name': {'family': 'Nakka', 'given': 'Yashwanth Kumar'}, 'orcid': '0000-0001-7897-3644'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2022
DOI: 10.1109/tro.2022.3197072
In this article, we present generalized polynomial chaos-based sequential convex programming (gPC-SCP) to compute a suboptimal solution for a continuous-time chance-constrained stochastic nonlinear optimal control (SNOC) problem. The approach enables motion planning for robotic systems under uncertainty. The gPC-SCP method involves two steps. The first step is to derive a surrogate problem of deterministic nonlinear optimal control (DNOC) with convex constraints by using gPC expansion and the distributionally robust convex subset of the chance constraints. The second step is to solve the DNOC problem using sequential convex programming for trajectory generation and control. We prove that in the unconstrained case, the optimal value of the DNOC converges to that of SNOC asymptotically and that any feasible solution of the constrained DNOC is a feasible solution of the chance-constrained SNOC. We also present the predictor–corrector extension (gPC-SCP_(PC)) for real-time motion trajectory generation in the presence of stochastic uncertainty. In the gPC-SCP_(PC) method, we first predict the uncertainty using the gPC method and then optimize the motion plan to accommodate the uncertainty. We empirically demonstrate the efficacy of the gPC-SCP and the gPC-SCP_(PC) methods for the following two test cases: first, collision checking under uncertainty in actuation and physical parameters and second, collision checking with stochastic obstacle model for 3DOF and 6DOF robotic systems. We validate the effectiveness of the gPC-SCP method on the 3DOF robotic spacecraft testbed.https://authors.library.caltech.edu/records/8ssa4-y5z12Rapid extraction of propeller geometry using photogrammetry
https://resolver.caltech.edu/CaltechAUTHORS:20221117-146764300.1
Authors: {'items': [{'id': 'Tang-Ellande', 'name': {'family': 'Tang', 'given': 'Ellande'}, 'orcid': '0000-0001-5933-4716'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2022
DOI: 10.1177/17568293221132044
As small Uninhabited Aerial Vehicles (sUAS) increase in popularity, computational analysis is increasingly being used to model and improve their performance. However, although propeller performance is one of the primary elements in modelling an aircraft, most manufacturers of propellers for this size of vehicle do not publish geometric information for the propeller. The lack of available geometric data makes simulation of propeller aerodynamics challenging. While techniques exist to accurately extract the 3D geometry of a propeller, these methods are often very expensive, time-consuming, or labor intensive. Additionally, typical 3D scanning techniques produce a 3D mesh that is not useful for techniques such as Blade Element Theory (BET), which rely on knowledge of the 2D cross sections along the propeller span. This paper describes a novel workflow to produce point clouds using readily available photo equipment and software and subsequently extract airfoil and propeller blade parameters at specified stations along the propeller span. The described process can be done with little theoretical knowledge of photogrammetry and with minimal human input. The propeller geometry generated is compared against results of established methods of geometry extraction and good agreement is shown.https://authors.library.caltech.edu/records/xhg13-ew961A general Bayesian nonlinear estimation method using resampled Smooth Particle Hydrodynamics solutions of the underlying Fokker–Planck Equation
https://resolver.caltech.edu/CaltechAUTHORS:20220705-346228000
Authors: {'items': [{'id': 'Duffy-Michael', 'name': {'family': 'Duffy', 'given': 'Michael'}, 'orcid': '0000-0002-6467-8748'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Bergman-Lawrence', 'name': {'family': 'Bergman', 'given': 'Lawrence'}, 'orcid': '0000-0003-3346-8649'}]}
Year: 2022
DOI: 10.1016/j.ijnonlinmec.2022.104134
The effectiveness of nonlinear filters depends on many factors, but one of the most important is how accurately the filter is able to predict the state dynamics of the underlying system between measurements. For a wide class of Gaussian white noise driven nonlinear systems the Bayesian optimal prior can be obtained by solving the system's corresponding Fokker–Planck Equation (FPE). Unfortunately the Fokker–Planck Equation is a partial differential equation with dimension equal to the number of states in the underlying dynamical system, making it extremely difficult to solve for realistic systems due to Curse of Dimensionality scaling issues. As a result it has been and still largely remains computationally impractical to simulate higher dimensional Fokker–Planck equations, at least while obtaining very high accuracy across the entire transient probability density function. This paper presents a general nonlinear filter based on solving the transient Fokker–Planck equation via Smooth Particle Hydrodynamics (SPH) at lower resolution, which turns out to still allow for accurate state estimation. The filter is enabled by an efficient heuristic resampling scheme of the SPH solution also presented here. The FPE-SPH Filter is able to replicate the accuracy of the Particle Filter and Extended Kalman filter (EKF) for lower-dimensional systems, while also being more robust than the EKF on certain classes of system.https://authors.library.caltech.edu/records/edffy-rwp85Rules of the Road: Formal Guarantees for Autonomous Vehicles With Behavioral Contract Design
https://resolver.caltech.edu/CaltechAUTHORS:20230411-764712100.4
Authors: {'items': [{'id': 'Cai-Karena-X', 'name': {'family': 'Cai', 'given': 'Karena X.'}, 'orcid': '0000-0002-8392-4158'}, {'id': 'Phan-Minh-Tung', 'name': {'family': 'Phan-Minh', 'given': 'Tung'}, 'orcid': '0000-0002-1403-5197'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}, {'id': 'Murray-R-M', 'name': {'family': 'Murray', 'given': 'Richard M.'}, 'orcid': '0000-0002-5785-7481'}]}
Year: 2023
DOI: 10.1109/tro.2023.3247951
The problem of safe and fair conflict resolution among inertial, distributed agents — particularly in highly interactive settings — is of paramount importance to the autonomous vehicles industry. The difficulty of solving this problem can be attributed to the fact that agents have to reason over other agents' complex behaviors. We propose the idea of using a behavioral contract to capture a set of explicitly defined assumptions about how all agents in the environment make decisions. In this article, we present a behavioral contract for a specific class of agents that can guarantee the safety and liveness (i.e., progress) of all agents operating in accordance with it. The behavioral contract has two main components—an ordered behavioral rulebook that the agent uses to select its intended action and some additional constraints that define when an agent has precedence (or not) to take its intended action. If all of the agents act according to this contract, we can guarantee safety under all traffic conditions and liveness for all agents under "sparse" traffic conditions. The formalism of the contract also enables assignment of blame. We provide proofs of correctness of the behavioral contract and validate our results in simulation.https://authors.library.caltech.edu/records/7sg3b-knb56Experiments and Modeling of the Ceiling Effect with Drone-Scale Propellers
https://resolver.caltech.edu/CaltechAUTHORS:20230530-441768000.72
Authors: {'items': [{'id': 'Tang-Ellande', 'name': {'family': 'Tang', 'given': 'Ellande'}, 'orcid': '0000-0001-5933-4716'}, {'id': 'Chung-Soon-Jo', 'name': {'family': 'Chung', 'given': 'Soon-Jo'}, 'orcid': '0000-0002-6657-3907'}]}
Year: 2023
DOI: 10.2514/1.j062568
Aircraft designs involving aerodynamic interactions between rotors and other aerodynamic surfaces are becoming increasingly popular. Studying these interactions is key to developing good models for design and aspects of the system such as control. This paper seeks to understand the effect of obstructing the upstream of a propeller at varying distances, also called the ceiling effect, by examining in depth the canonical case of a theoretically infinite obstruction and its interaction with a propeller or actuator disk. Experimental studies of force and pressure are compared to results from computational fluid dynamics (CFD), and good agreement is shown. The CFD results are then compared against the Morillo flowfield model, and a correction is found to match the results. The data indicate that some propellers experienced a nearly twofold increase in thrust. However, a matching force on the surface develops, and the net force drops to nearly zero. The force interaction between the two nearly disappears once the separation exceeds half a propeller diameter. These results are independent of propeller size and pitch. The implemented theoretical model also has low computational cost, and it could be used to improve low-order models such as panel methods or provide a foundation for future rotor–body interaction modeling.https://authors.library.caltech.edu/records/qpmpg-p1h18