用生态学规则指导机器人跨队协作，提升复杂救援系统效率
Learning Altruistic Collaboration in Heterogeneous Multi-Team Systems
- 基于哈密顿规则设计跨队资源分配机制
- 在消防模拟中逼近最优解并支持大规模系统
- 适合多智能体协作与动态资源调度研究者
本文研究通过动态机器人调配实现异构多团队协作，将机器人视为可转移资源。借鉴生态学中的哈密顿规则作为利他决策机制，提出一种考虑异构能力、转移成本及能力相关贡献的多团队协作资源分配框架。该分配问题为组合优化问题，被证明是NP-hard。为解决可扩展性问题，我们设计了一个基于图神经网络的策略，在集中训练、分散执行框架下，近似基于哈密顿规则的利他分配。模型在团队交互图上运行，预测机器人级转移决策和下一任务分配。所提方法在消防救援场景中通过仿真与实验验证，表明学习到的策略在保持近似最优性能的同时，可扩展至更大规模系统。
原文摘要 · Abstract (English)
This paper studies heterogeneous multi-team collaboration through dynamic robot allocation, where robots are treated as transferable resources. Leveraging Hamilton's rule from ecology as an altruistic decision-making mechanism, we propose a multi-team collaborative resource allocation framework with heterogeneous capabilities, transfer costs, and capability-dependent contributions. The resulting allocation problem is combinatorial and is shown to be NP-hard. To address scalability, we develop a graph neural network policy under centralized training and decentralized execution that approximates the altruistic allocations based on Hamilton's rule. The model operates over the team interaction graph and predicts robot-level transfer decisions and next robot-to-team assignments. The proposed approach is validated in a firefighting scenario through simulations and experiments, demonstrating that the learned policy achieves near-optimal performance while scaling to larger systems.
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