Hierarchical Planning for Heterogeneous Multi-Robot Routing Problems Via Learned Subteam Performance

Abstract

This paper considers a particular class of multi-robot task allocation problems, where tasks correspond to heterogeneous multi-robot routing problems defined on different areas of a given environment. We present a hierarchical planner that breaks down the complexity of this problem into two subproblems: the high-level problem of allocating robots to routing tasks, and the low-level problem of computing the actual routing paths for each subteam. The planner uses a Graph Neural Network (GNN) as a heuristic to estimate subteam performance for specific coalitions on specific routing tasks. It then iteratively refines the estimates to the real subteam performances as solutions of the low-level problems become available. On a testbed problem of a heterogeneous multi-robot area inspection problem as the base routing task, we empirically show that our hierarchical planner is able to compute optimal or near-optimal (within 7%) solutions approximately 16 times faster (on average) than an optimal baseline that computes plans for all the possible allocations in advance to obtain precise routing times. Furthermore, we show that a GNN-based estimator can provide an excellent trade-off between solution quality and computation time compared to other baseline (non-learned) estimators.