Dual-CBS: A Hierarchical Approach via Conflict-Based Search and Sampling for Multi-Agent Motion Planning

Joonyeol Sim, Shao-Hung Chan, Sven Koenig

Keywords: Multi-Robot Systems, Multiagent Systems, Multiagent Planning

TL;DR: Dual-CBS hierarchically combines grid-based planning and trajectory sampling for multi-agent motion planning, achieving ~10x lower travel times than SSSP.

Abstract: Multi-Agent Motion Planning is a problem of finding a set of collision-free trajectories, one for each agent, to move from their start configurations to their goal configurations while minimizing the sum of travel time. To solve MAMP, we propose Dual Conflict-Based Search (Dual-CBS), which combines the search and sampling approaches in a hierarchical manner. Dual-CBS first decouples the configuration space into grids and finds a set of grid-based paths. Then, it samples one trajectory for each agent, which is a sequence of configurations within its grid-based path. In comparison to approaches that find collision-free trajectories on a shared roadmap, Dual-CBS does not introduce heavy computational overhead in constructing such a roadmap. Meanwhile, it maintains the mobility of agents for efficiently resolving collisions. In comparison to the state-of-the-art MAMP approach, Simultaneous Sampling-and-Search (SSSP), which heavily relies on local collision avoidance, Dual-CBS guides the sampled trajectories with the grid-based paths and thus finds solutions with a lower sum of travel time.

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Submission Number: 29