Scalable Multi-robot Motion Planning for Congested Environments With Topological Guidance
This addresses coordination challenges for robots in constrained settings like warehouses, representing an incremental extension of existing methods.
The paper tackled the problem of multi-robot motion planning in congested environments with narrow passages, extending a topology-guided single-robot method to achieve scalability up to 25 times larger robot teams and find higher-quality solutions.
Multi-robot motion planning (MRMP) is the problem of finding collision-free paths for a set of robots in a continuous state space. The difficulty of MRMP increases with the number of robots and is exacerbated in environments with narrow passages that robots must pass through, like warehouse aisles where coordination between robots is required. In single-robot settings, topology-guided motion planning methods have shown improved performance in these constricted environments. In this work, we extend an existing topology-guided single-robot motion planning method to the multi-robot domain to leverage the improved efficiency provided by topological guidance. We demonstrate our method's ability to efficiently plan paths in complex environments with many narrow passages, scaling to robot teams of size up to 25 times larger than existing methods in this class of problems. By leveraging knowledge of the topology of the environment, we also find higher-quality solutions than other methods.