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Abstract: multi-mobile robot systems (MMRSs) are widely used for transportation in industrial scenes such as manufacturing and warehousing. In an MMRS, motion coordination is important as collisions and deadlocks may lead to losses or system stagnation. However, in some scenarios, robot sizes are different when loaded and unloaded, which means that the robots are variable-sized, making motion coordination more difficult. The methods based on zone control need to first divide the environment into disjoint zones, and then allocate the zones statically or dynamically for motion coordination. The zone-control-based methods are not accurate enough for variable-sized multi-mobile robots and reduce the efficiency of the system. This paper describes a motion coordination method based on glued nodes, which can dynamically avoid collisions and deadlocks according to the roadmap structure and the real-time paths of robots. Dynamic features make this method directly applicable to various scenarios, instead of dividing a roadmap into disjoint zones. The proposed method has been applied to many industrial projects, and this study is based on some manufacturing projects for experiments. Theoretical analysis and experimental results show that the proposed algorithm is effective and efficient.

一种新型变尺寸多移动机器人的运动协同方法

[1]Alonso-Mora J, Breitenmoser A, Rufli M, et al., 2013. Optimal reciprocal collision avoidance for multiple non-holonomic robots. 10^th Int Symp on Distributed Autonomous Robotic Systems, p.203-216.

[2]Alonso-Mora J, DeCastro JA, Raman V, et al., 2018. Reactive mission and motion planning with deadlock resolution avoiding dynamic obstacles. Auton Robot, 42(4):801-824.

[3]Chen HF, Wu NQ, Zhou MC, 2016. A novel method for deadlock prevention of AMS by using resource-oriented Petri nets. Inform Sci, 363:178-189.

[4]Coffman EG, Elphick M, Shoshani A, 1971. System deadlocks. ACM Comput Surv, 3(2):67-78.

[5]de Ryck M, Versteyhe M, Debrouwere F, 2020. Automated guided vehicle systems, state-of-the-art control algorithms and techniques. J Manuf Syst, 54:152-173.

[6]Draganjac I, Petrović T, Miklić D, et al., 2020. Highly-scalable traffic management of autonomous industrial transportation systems. Robot Comput-Integr Manuf, 63:101915.

[7]Fanti MP, Mangini AM, Pedroncelli G, et al., 2015. Decentralized deadlock-free control for AGV systems. American Control Conf, p.2414-2419.

[8]Guruji AK, Agarwal H, Parsediya DK, 2016. Time-efficient A* algorithm for robot path planning. Proc Technol, 23:144-149.

[9]Habermann AN, 1969. Prevention of system deadlocks. Commun ACM, 12(7):373-377,385.

[10]Jager M, Nebel B, 2001. Decentralized collision avoidance, deadlock detection, and deadlock resolution for multiple mobile robots. Proc IEEE/RSJ Int Conf on Intelligent Robots and Systems, Expanding the Societal Role of Robotics in the Next Millennium, p.1213-1219.

[11]Krnjak A, Draganjac I, Bogdan S, et al., 2015. Decentralized control of free ranging AGVs in warehouse environments. IEEE Int Conf on Robotics and Automation, p.2034-2041.

[12]Li Q, Pogromsky A, Adriaansen T, et al., 2016. A control of collision and deadlock avoidance for automated guided vehicles with a fault-tolerance capability. Int J Adv Robot Syst, 13(2):64.

[13]Luo JL, Wan YX, Wu WM, et al., 2020. Optimal Petri-net controller for avoiding collisions in a class of automated guided vehicle systems. IEEE Trans Intell Transp Syst, 21(11):4526-4537.

[14]Małopolski W, 2018. A sustainable and conflict-free operation of AGVs in a square topology. Comput Ind Eng, 126:472-481.

[15]Moorthy RL, Hock-Guan W, Wing-Cheong N, et al., 2003. Cyclic deadlock prediction and avoidance for zone-controlled AGV system. Int J Prod Econ, 83(3):309-324.

[16]Reveliotis S, 2020. An MPC scheme for traffic coordination in open and irreversible, zone-controlled, guidepath-based transport systems. IEEE Trans Autom Sci Eng, 17(3):1528-1542.

[17]Tarjan R, 1971. Depth-first search and linear graph algorithms. 12^th Annual Symp on Switching and Automata Theory, p.114-121.

[18]Wang X, Kloetzer M, Mahulea C, et al., 2015. Collision avoidance of mobile robots by using initial time delays. 54^th IEEE Conf on Decision and Control, p.324-329.

[19]Willms AR, Yang SX, 2006. An efficient dynamic system for real-time robot-path planning. IEEE Trans Syst Man Cybern Part B Cybern, 36(4):755-766.

[20]Wu NQ, Zhou MC, 2007. Deadlock resolution in automated manufacturing systems with robots. IEEE Trans Autom Sci Eng, 4(3):474-480.

[21]Xing KY, Wang F, Zhou MC, et al., 2018. Deadlock characterization and control of flexible assembly systems with Petri nets. Automatica, 87:358-364.

[22]Xing ZC, Chen XY, Wang XK, et al., 2022. Collision and deadlock avoidance in multi-robot systems based on glued nodes. IEEE/CAA J Autom Sin, 9(7):1327-1330.

[23]Yoo JW, Sim ES, Cao CX, et al., 2005. An algorithm for deadlock avoidance in an AGV system. Int J Adv Manuf Technol, 26(5):659-668.

[24]Yu JJ, LaValle SM, 2016. Optimal multirobot path planning on graphs: complete algorithms and effective heuristics. IEEE Trans Robot, 32(5):1163-1177.

[25]Zając J, Małopolski W, 2021. Structural on-line control policy for collision and deadlock resolution in multi-AGV systems. J Manuf Syst, 60:80-92.

[26]Zhang LJ, Kim YJ, Manocha D, 2007. A hybrid approach for complete motion planning. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.7-14.

[27]Zhao YL, Liu XP, Wu SB, et al., 2021. Spare zone based hierarchical motion coordination for multi-AGV systems. Simul Model Pract Theory, 109:102294.

[28]Zhong MS, Yang YS, Dessouky Y, et al., 2020. Multi-AGV scheduling for conflict-free path planning in automated container terminals. Comput Ind Eng, 142:106371.

[29]Zhou Y, Hu HS, Liu Y, et al., 2020. A distributed method to avoid higher-order deadlocks in multi-robot systems. Automatica, 112:108706.