Distributed control of cooperative target enclosing based on reachability and invariance analysis

Liu, Ying; Yan, Gangfeng; Lin, Zhiyun

doi:10.1016/j.sysconle.2010.04.003

Cited by 122 publications

(62 citation statements)

References 20 publications

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“…They are kinematically controlled, and the feasible control range is bounded. The speed range is bounded from above unlike all papers in the area except Lan et al, 20 and from below unlike Lan et al 20 The latter complies with the capabilities of, e.g., fixed-wing aerial vehicles or certain classes of autonomous underwater robots, 2, 25 and simultaneously provides an additional challenge by restricting the paths along which the robot can travel to planar curves with upper limited curvatures.…”

Section: Introductionmentioning

confidence: 94%

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Distributed control of multiple non-holonomic robots with sector vision and range-only measurements for target capturing with collision avoidance

et al. 2014

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Link to this article: http://journals.cambridge.org/abstract_S0263574714000320How to cite this article: A. Zakhar'eva, A. S. Matveev, M. C. Hoy and A. V. Savkin (2015). Distributed control of multiple non-holonomic robots with sector vision and range-only measurements for target capturing with collision avoidance. Robotica, 33, pp 385-412 SUMMARYWe consider a team of autonomous kinematically controlled non-holonomic planar Dubins car-like vehicles. The team objective is to encircle a given target so that all vehicles achieve a common and pre-specified distance from it and are uniformly distributed over the respective circle, and the entire formation rotates around the target with a prescribed angular velocity. The robots do not communicate with each other and any central decision-maker. The sensing capacity of any vehicle is heavily restricted: It has access only to the distance to the target and to the distances to the companion vehicles that are in a given disc sector centered at the vehicle at hand; no robot can distinguish between its companions, and does not know their parameters. A distributed control law is proposed, and mathematically rigorous proofs of its non-local convergence as well as collision avoidance property are presented. The performance of the control law is illustrated by computer simulations and experiments with real robots.

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Section: Introductionmentioning

confidence: 94%

“…[13,20,30]. The control strategy from El Kamel et al 13 is aimed at driving a team of fully actuated unconstrained unicycles with all-to-all communication capability to a prescribed distance from a steady target while avoiding inter-vehicle collisions.…”

Section: Introductionmentioning

confidence: 99%

Distributed control of multiple non-holonomic robots with sector vision and range-only measurements for target capturing with collision avoidance

et al. 2014

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“…The objective was to grasp the target with a team of robots, to enclose the target at a pre-specified distance from it, to uniformly distribute the robots around the target, and to subsequently escort the target, with maintaining the distances both to the target and between the robots. In contrast with the majority of relevant algorithms available in the literature (see, e.g., [47,48,49,50,28,51,43,52,53,54,32,55,56,57,58,29,44,59,60,61,62] for representative samples; a detailed literature overview is available in [30,62]), angular data about the target are not available to the robots, and the objective should be achieved based on only range measurements. Range-only based navigation is of interest for many applications since it carries a potential both for reduction of the cost and complexity of the hardware and for increase of the system reliability [63,64,39].…”

Section: Computer Simulation Testsmentioning

confidence: 99%

“…For target capturing scenarios, the issue of collision avoidance was addressed in [42,43,44]. However, either the robots were not prevented from getting into a cluster [42], or the circular equidistant curve was assumed to be known prior to the maneuver [42,44], or a team of only three robots was treated [43], or the focus was on maneuvers in a close proximity of the target [44].…”

Section: Introductionmentioning

confidence: 99%

Cooperative surveillance of unknown environmental boundaries by multiple nonholonomic robots

Ovchinnikov

Semakova

Matveev

2015

Robotics and Autonomous Systems

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“…Much of the current literature on multiple cooperative UAVs pays particular attention to dynamic encirclement. In real life, dynamic encirclement has been used in defending a secure airspace against an invading aircraft, maintaining surveillance over a ground target and in protecting the borders against invading targets [20,22,23,24] . For instance, in [25] , the problem of capturing a target with a group of cooperative UAVs was solved using a complete dynamic network topology based on the relative distance between the members of the team.…”

Section: Introductionmentioning

confidence: 99%

Multi-UAV Tactic Switching via Model Predictive Control and Fuzzy Q-Learning

Hafez

Givigi

Yousefi³

et al. 2017

Eng. Sci. and Milit. Techno.

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Teams of cooperative Unmanned Aerial Vehicles (UAVs) require intelligent and flexible control strategies to allow the accomplishment of a multitude of challenging group tasks. In this paper, we introduce a solution for the problem of tactic switching between the formation flight tactic and the dynamic encirclement tactic for a team of cooperative UAVs using a predictive decentralize control approach. Decentralized Model Predictive Control (MPC) is used to generate tactics for a team of UAVs in simulation and real-world validation. A high-level Linear Model Predictive Control (LMPC) policy is used to control the UAV team during the execution of a desired formation, while a combination of decentralized LMPC and Feedback Linearization (FL) is applied to the UAV team to accomplish dynamic encirclement. The decision of switching from one tactic to the other is derived by a fuzzy logic controller, which, in its turn, is derived by a Reinforcement Learning (RL) approach. The main contributions of this paper are: (i) solution of the problem of tactic switching for a team of cooperative UAVs using LMPC and a fuzzy controller derived via RL; (ii) simulations demonstrating the efficiency of the method; and (iii) implementation of the solution to on-board real-time controllers on Qball-X4 quadrotors.

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Distributed control of cooperative target enclosing based on reachability and invariance analysis

Cited by 122 publications

References 20 publications

Distributed control of multiple non-holonomic robots with sector vision and range-only measurements for target capturing with collision avoidance

Distributed control of multiple non-holonomic robots with sector vision and range-only measurements for target capturing with collision avoidance

Cooperative surveillance of unknown environmental boundaries by multiple nonholonomic robots

Multi-UAV Tactic Switching via Model Predictive Control and Fuzzy Q-Learning

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