Abstract
In this paper, a novel path planning algorithm for multiple robots using congestion analysis and control is presented. The algorithm ensures a safe path planning solution by avoiding collisions among robots as well as among robots and humans. For each robot, alternative paths to the goal are realised. By analysing the travelling time of robots on different paths using Petri Nets, the optimal configuration of paths is selected. The prime objective is to avoid congestion when routing many robots into a narrow area. The movements of robots are controlled at every intersection by organising a one-by-one passing of the robots. Controls are available for the robots which are able to communicate and share information with each other. To avoid collision with humans and other moving objects (i.e. robots), a dipole field integrated with a dynamic window approach is developed. By considering the velocity and direction of the dynamic obstacles as sources of a virtual magnetic dipole moment, the dipole-dipole interaction between different moving objects will generate repulsive forces proportional to the velocity to prevent collisions. The whole system is presented on the widely used platform Robot Operating System (ROS) so that its implementation is extendable to real robots. Analysis and experiments are demonstrated with extensive simulations to evaluate the effectiveness of the proposed approach.
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Open access funding provided by Mälardalen University. The research leading to the presented results has been undertaken within the research profile DPAC - Dependable Platform for Autonomous Systems and Control, funded by the Swedish Knowledge Foundation.
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All authors, including Lan Anh Trinh, Mikael Ekström and Baran Cürüklü, contributed to the writing of the manuscript. Lan Anh Trinh implemented the algorithms and performed the testing experiments.
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Trinh, L.A., Ekström, M. & Cürüklü, B. Dependable Navigation for Multiple Autonomous Robots with Petri Nets Based Congestion Control and Dynamic Obstacle Avoidance. J Intell Robot Syst 104, 69 (2022). https://doi.org/10.1007/s10846-022-01589-1
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DOI: https://doi.org/10.1007/s10846-022-01589-1