mlsanityData to 14 Oct 2026

Onboard Mission Replanning for Adaptive Cooperative Multi-Robot Systems

arXiv 2506.06094 ↗First posted 6 Jun 2025cs.RO cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 40 references that are in the map. First posted on arXiv 6 Jun 2025.

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11 of 40 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Neural Combinatorial Optimization with Reinforcement Learningcited by 608
  3. 2018Reinforcement Learning for Solving the Vehicle Routing Problemcited by 333
  4. 2020Reinforcement Learning for Combinatorial Optimization: A Surveycited by 236
  5. 2023RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmarkcited by 71
  6. 2024MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Expertscited by 70
  7. 2024DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problemscited by 18
  8. 2020Influence of Initialization on the Performance of Metaheuristic Optimizerscited by 5
  9. 2021Hybrid Pointer Networks for Traveling Salesman Problems Optimizationcited by 4
  10. 2016Optimizing Communication and Computation for Multi-UAV Information Gathering Applicationscited by 3
  11. 2022K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agentscited by 2

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  1. 2025Graph Based Deep Reinforcement Learning Aided by Transformers for Multi-Agent Cooperation
  2. 2026Learning to Adapt: Representation-Based Reinforcement Learning for Multi-Task Skill Transfer
  3. 2026Safe and Scalable Multi-Drone Payload Transport via CBF-based Reinforcement Learning with Zero-Shot Sim-to-Real Transfer
  4. 2025ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordination
  5. 2024Energy-Aware Multi-Agent Reinforcement Learning for Collaborative Execution in Mission-Oriented Drone Networks
  6. 2026Agentic Self-Evolutionary Replanning for Embodied Navigation
  7. 2026Learning Adaptive Multi-Task Guidance, Navigation, and Control via Hypernetworks
  8. 2024Distributed Autonomous Swarm Formation for Dynamic Network Bridging