Suboptimal Variants of the Conflict-Based Search Algorithm for the Multi-Agent Pathfinding Problem

By Max Barer, Guni Sharon, Roni Stern, Ariel Felner. Symposium on Combinatorial Search, 2014

426 citations recorded by the source corpus.

The task in the multi-agent path finding problem (MAPF) is to find paths for multiple agents, each with a different start and goal position, such that agents do not collide. A successful optimal MAPF solver is the conflict-based search (CBS) algorithm. CBS is a two level algorithm where special conditions ensure it returns the optimal solution. Solving MAPF optimally is proven to be NP-hard, hence CBS and all other optimal solvers do not scale up. We propose several ways to relax the optimality conditions of CBS trading solution quality for runtime as well as bounded-suboptimal variants, where the returned solution is guaranteed to be within a constant factor from optimal solution cost. Experimental results show the benefits of our new approach; a massive reduction in running time is presented while sacrificing a minor loss in solution quality. Our new algorithms outperform other existing algorithms in most of the cases.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/c1439fdf4b85fdfc01a4684bb264030a1a3ba1f8

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Citations in the corpus

Subdimensional expansion for multirobot path planning (2015, methodology): “SAT planners have also been used to find shortcuts to reduce the cost of nonoptimal paths computed by rule-based planners [27, 28].”
PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning (2018, background): “To support these applications, significant research effort has been devoted to multi-agent path finding (MAPF) [3], [4], [5], [6] for deployment in distribution centers and potential use for airplane taxiing [7], [8].”
Mobile Robot Path Planning in Dynamic Environments Through Globally Guided Reinforcement Learning (2020, methodology): “ECBS andHCA∗ achieve the best performance overall, since they are both centralized approaches that have access to all robots’ trajectory information.”
Trajectory Planning for Quadrotor Swarms (2018, methodology): “The proof in [28] holds for the ECBS/C as well.”
A review of cooperative multi-agent deep reinforcement learning (2019, background): “searching in high-dimensional spaces by building a set of constraints and planning for each agent [14, 179].”