Scalable Multi-robot Motion Planning via Hierarchical Subproblem Expansion and Workspace Decomposition Refinement

2026-05-19

AI Summary

Multi-robot motion planning faces significant challenges due to the high dimensionality of the joint configuration space, which incurs substantial computational costs and coordination difficulties. This work proposes an iteratively refined workspace decomposition approach that enables efficient cooperative path planning by hierarchically expanding subproblems and performing discrete search within decoupled, low-dimensional configuration spaces, thereby avoiding explicit construction of the high-dimensional joint space. By preserving coordination guarantees while substantially reducing computational complexity, the method achieves up to an order-of-magnitude improvement in planning speed compared to existing techniques, significantly enhancing the scalability and real-time performance of multi-robot systems.

Abstract

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.

Problem

• multi-robot motion planning
• coordination
• computational complexity
• configuration space
• workspace decomposition

Innovation

• multi-robot motion planning
• workspace decomposition
• hierarchical planning
• configuration space decoupling
• scalable coordination