多机器人系统在异构环境下实现纳什均衡的分布式求解方法
Distributed Nash Equilibrium Seeking Algorithm in Aggregative Games for Heterogeneous Multi-Robot Systems
- 通过邻近机器人共享信息，设计分布式优化与输出控制协同机制
- 算法收敛且能实现高效系统决策，仿真与实机测试均验证有效
- 适用于异构多机器人协作场景，尤其适合资源受限的分布式系统
本文提出一种针对异构多机器人系统的分布式纳什均衡求解算法。该算法利用分布式优化与输出控制，通过邻近机器人间的信息共享，实现纳什均衡的达成。具体而言，我们设计了一种分布式优化算法，为每台机器人生成特定参考值以逼近纳什均衡，并为异构多机器人系统设计输出控制律，使其在聚合博弈中跟踪该参考值。理论证明该算法具有全局收敛性并可产生高效结果。通过数值仿真和物理机器人实测，验证了所提方法的有效性。
原文摘要 · Abstract (English)
This paper develops a distributed Nash Equilibrium seeking algorithm for heterogeneous multi-robot systems. The algorithm utilises distributed optimisation and output control to achieve the Nash equilibrium by leveraging information shared among neighbouring robots. Specifically, we propose a distributed optimisation algorithm that calculates the Nash equilibrium as a tailored reference for each robot and designs output control laws for heterogeneous multi-robot systems to track it in an aggregative game. We prove that our algorithm is guaranteed to converge and result in efficient outcomes. The effectiveness of our approach is demonstrated through numerical simulations and empirical testing with physical robots.
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