Abstract
Abstract
This paper proposes a novel two-layer framework based on conflict-based search and regional divisions to improve the efficiency of multi-robot path planning. The high-level layer targets the reduction of conflicts and deadlocks, while the low-level layer is responsible for actual path planning. Distinct from previous dual-level search frameworks, the novelties of this work are (1) subdivision of planning regions for each robot to decrease the number of conflicts encountered during planning; (2) consideration of the number of robots in the region during planning in the node expansion stage of A*, and (3) formal proof demonstrating the nonzero probability of the proposed method in obtaining a solution, along with providing the upper bound of the solution in a special case. Experimental comparisons with Enhanced Conflict-Based Search demonstrate that the proposed method not only reduces the number of conflicts but also achieves a computation time reduction of over 30%.
Publisher
Cambridge University Press (CUP)
Reference28 articles.
1. EECBS: A bounded-suboptimal search for multi-agent path finding;Li;Proceed AAAI Conf Artif Intell,2021
2. Icbs: The improved conflict-based search algorithm for multi-agent pathfinding;Boyarski;Proceed Int Symp Combin Sear,2015
3. Optimal Reciprocal Collision Avoidance for Multiple Non-Holonomic Robots
4. Multi‐robot path planning based on a deep reinforcement learning DQN algorithm
5. ECBS with flex distribution for bounded-suboptimal multi-agent path finding;Chan;Proceed Int Symp Combin Sear,2021