Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/83439
Title: Collision and Deadlock Avoidance in Multirobot Systems: A Distributed Approach
Authors: Zhou, Yuan
Hu, Hesuan
Liu, Yang
Ding, Zuohua
Keywords: Discrete event systems
Collision and deadlock avoidance
Issue Date: 2017
Source: Zhou, Y., Hu, H., Liu, Y., & Ding, Z. (2017). Collision and Deadlock Avoidance in Multirobot Systems: A Distributed Approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(7), 1712-1726.
Series/Report no.: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Abstract: Collision avoidance is a critical problem in motion planning and control of multirobot systems. Moreover, it may induce deadlocks during the procedure to avoid collisions. In this paper, we study the motion control of multirobot systems where each robot has its own predetermined and closed path to execute persistent motion. We propose a real-time and distributed algorithm for both collision and deadlock avoidance by repeatedly stopping and resuming robots. The motion of each robot is first modeled as a labeled transition system, and then controlled by a distributed algorithm to avoid collisions and deadlocks. Each robot can execute the algorithm autonomously and real-timely by checking whether its succeeding state is occupied and whether the one-step move can cause deadlocks. Performance analysis of the proposed algorithm is also conducted. The conclusion is that the algorithm is not only practically operative but also maximally permissive. A set of simulations for a system with four robots are carried out in MATLAB. The results also validate the effectiveness of our algorithm.
URI: https://hdl.handle.net/10356/83439
http://hdl.handle.net/10220/43545
ISSN: 2168-2216
DOI: 10.1109/TSMC.2017.2670643
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TSMC.2017.2670643].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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