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Math Biosci Eng

Title:		Conflict-free and energy-efficient path planning for multi-robots based on priority free ant colony optimization
Author(s):		Li P; Yang L;
Address:		"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650093, China. Faculty of Information Engineering, Xinjiang Institute of Technology, Akesu 843100, China"
Journal Title:		Math Biosci Eng
Year:		2023
Volume:		20221206
Issue:		2
Page Number:		3528 - 3565
DOI:		10.3934/mbe.2023165
ISSN/ISBN:		1551-0018 (Electronic) 1547-1063 (Linking)
Abstract:		"With the background of limited energy storage of robots and considering the high coupling problem of multi-agent path finding (MAPF), we propose a priority-free ant colony optimization (PFACO) to plan conflict-free and energy-efficient paths, reducing multi-robots motion cost in the rough ground environment. First, a dual-resolution grid map considering obstacles and ground friction factors is designed to model the unstructured rough terrain. Second, an energy-constrained ant colony optimization (ECACO) is proposed to achieve energy-optimal path planning for a single robot, in which we improve the heuristic function based on the combined effects of path length, path smoothness, ground friction coefficient and energy consumption, and consider multiple energy consumption metrics during robot motion to improved pheromone update strategy. Finally, considering multiple collision conflict cases among multiple robots, we incorporate a prioritized conflict-free strategy (PCS) and a route conflict-free strategy (RCS) based on ECACO to achieve MAPF with low-energy and conflict-free in a rough environment. Simulation and experimental results show that ECACO can achieve better energy saving for single robot motion under all three common neighborhood search strategies. PFACO achieves both the conflict-free path and energy-saving planning for robots in complex scenarios, and the study has some reference value for solving practical problems"
Keywords:		ant colony optimization conflict-free energy-efficient multi-agent path finding unstructured rough terrain;
Notes:		"PubMed-not-MEDLINELi, Ping Yang, Liwei eng 2023/03/12 Math Biosci Eng. 2023 Jan; 20(2):3528-3565. doi: 10.3934/mbe.2023165. Epub 2022 Dec 6"