Scalable Multi-robot Motion Planning via Hierarchical Subproblem Expansion and Workspace Decomposition Refinement
This work addresses the computational bottleneck of multi-robot motion planning by improving planning efficiency for mobile robot teams.
The paper presents a method for multi-robot motion planning that reduces planning time by up to an order of magnitude by using discrete search over a workspace decomposition to coordinate robots and iteratively refining the workspace representation to search smaller, decoupled configuration spaces.
A fundamental challenge in multi-robot motion planning is achieving sufficient coordination to avoid inter-robot conflicts without incurring the large computational expense of searching the joint configuration space of the robot group. In this work, we present a method for multiple mobile robot motion planning that achieves an improvement in planning time up to an order of magnitude by leveraging the insight that we can use discrete search over a workspace decomposition to provide coordination between robots during planning. While prior work uses workspace topology to inform when coordination between robots is needed and then composes robots into their joint configuration space, we take a step further by iteratively refining our workspace representation to allow our planner to search smaller, decoupled configuration spaces.