DDM: Fast Near-Optimal Multi-Robot Path Planning Using Diversified-Path and Optimal Sub-Problem Solution Database Heuristics

• Published in: IEEE robotics and automation letters Vol. 5; no. 2; pp. 1349 - 1356
• Main Authors: Han, Shuai D., Yu, Jingjin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Apexes; Collision avoidance; Decoupling; Heuristic; Heuristic algorithms; multi-robot systems; Multiple robots; Optimization; Path planning; Path planning for multiple mobile robots or agents; Planning; Robot kinematics; Robots; scheduling and coordination; Warehouses
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2020.2967326

We propose a novel centralized and decoupled algorithm, DDM, for solving multi-robot path planning problems in grid graphs, targeting on-demand and automated warehouse-like settings. Two settings are studied: a traditional one whose objective is to move a set of robots from their respective initial vertices to the goal vertices as quickly as possible, and a dynamic one which requires frequent re-planning to accommodate for goal configuration adjustments. Among other techniques, DDM is mainly enabled through exploiting two innovative heuristics: path diversification and optimal sub-problem solution databases. The two heuristics attack two distinct phases of a decoupling-based planner: while path diversification allows the more effective use of the entire workspace for robot travel, optimal sub-problem solution databases facilitate the fast resolution of local path conflicts. Extensive evaluation demonstrates that DDM achieves high levels of scalability and solution quality close to the optimum.