On-board Mission Replanning for Adaptive Cooperative Multi-Robot Systems

• URL: http://arxiv.org/abs/2506.06094v1
• Date: Fri, 06 Jun 2025 13:54:19 GMT
• Title: On-board Mission Replanning for Adaptive Cooperative Multi-Robot Systems
• Authors: Elim Kwan, Rehman Qureshi, Liam Fletcher, Colin Laganier, Victoria Nockles, Richard Walters,
• Abstract summary: Cooperative autonomous robotic systems have significant potential for executing complex multi-task missions.<n>They commonly operate in remote, dynamic and hazardous environments.<n>Fast, on-board replanning algorithms are therefore needed to enhance resilience.<n>This work paves the way for increased resilience in autonomous multi-agent systems.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 reliance on fragile or slow communication links to centralised compute. Fast, on-board replanning algorithms are therefore needed to enhance resilience. Reinforcement Learning shows strong promise for efficiently solving mission planning tasks when 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 define the Cooperative Mission Replanning Problem as a novel variant of multiple TSP with adaptations to overcome these issues, and develop a new encoder/decoder-based model using Graph Attention Networks and Attention Models 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.

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