Collision Avoidance Pathfinding of Multiple AGVs Considering Motion Uncertainties
Mingxiao Chen, Shuting Wang, Yifei Li, Li Hu, Yuanlong Xie, Tifan Xiong · IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society · 2022
The multiple automatic guided vehicles (AGVs) pathfinding methods that do not consider the uncertainties of the future motion state may lead to large-scale congestion. To address this problem, this paper proposes an improved conflict-based search (CBS) algorithm with a load-information map. Firstly, the grid map with load information and the evaluation criteria of collision-free pathfinding is constructed. Then, an improved CBS algorithm is designed under a set of constraints on the motion states. This is achieved by performing a search on the constrained conflict tree between individual robots using load-information-map at the high level while exploring efficient single-robot searches with modified penalty function at the low level. Finally, the proposed improved CBS algorithm is tested through simulation and experiment. The results show that it not only finds a collision-free path for multiple AGVs under the motion uncertainties but also obtains a comprehensive optimal solution that considers the evaluation criteria and map load simultaneously.