Onboard Mission Replanning for Adaptive Cooperative Multi-Robot Systems

Abstract

Cooperative autonomous robotic systems have significant potential for executing complex multi-task missions across space, air, ground, and maritime domains. But they commonly operate in remote, dynamic and hazardous environments, requiring rapid in-mission adaptation without relying on fragile or slow communication links to centralized compute. Fast, on-board replanning algorithms are therefore essential to enhance resilience for these systems, but do not yet exist. Reinforcement Learning (RL) shows strong promise for efficiently solving mission planning tasks formulated as Travelling Salesperson Problems (TSPs), but existing methods: 1) are unsuitable for replanning, where agents do not start at a single location; 2) do not allow cooperation between agents; 3) are unable to model tasks with variable durations; or 4) lack practical considerations for on-board deployment. Here we address this gap by defining the Cooperative Mission Replanning Problem as a novel adaptation of multiple TSP, and develop a new encoder/decoder-based RL model to solve it effectively and efficiently. Using a simple example of cooperative drones, we show our replanner consistently (90% of the time) maintains performance within 10% of the state-of-the-art LKH3 heuristic solver, whilst running 85-370 times faster on a Raspberry Pi. This work paves the way for increased resilience in autonomous multi-agent systems.