多智能体在磁悬浮平台上的安全运动规划，提升扩展性与实时性。
Multi-Agent Motion Planning on Industrial Magnetic Levitation Platforms: A Hybrid ADMM-HOCBF approach
- 用分布式ADMM结合集中式高阶控制屏障函数实现协同规划
- 仿真显示扩展性远超传统集中式MPC，支持更多智能体
- 真实工业磁悬浮平台验证，具备实时部署能力
本文提出一种针对非完整多智能体系统的新型混合运动规划方法。所提出的分布式模型预测控制（MPC）框架克服了随智能体数量增加而变得不可行的传统集中式MPC问题，同时提供安全性保障。该方法通过将分布式交替方向乘子法（ADMM）与集中式高阶控制屏障函数（HOCBF）架构相结合实现。仿真结果表明，该方法在可扩展性方面显著优于经典集中式MPC。通过开发高效的C++实现，并将生成的轨迹部署到真实的工业磁悬浮平台上，验证了该方法的有效性与实时能力。
原文摘要 · Abstract (English)
This paper presents a novel hybrid motion planning method for holonomic multi-agent systems. The proposed decentralised model predictive control (MPC) framework tackles the intractability of classical centralised MPC for a growing number of agents while providing safety guarantees. This is achieved by combining a decentralised version of the alternating direction method of multipliers (ADMM) with a centralised high-order control barrier function (HOCBF) architecture. Simulation results show significant improvement in scalability over classical centralised MPC. We validate the efficacy and real-time capability of the proposed method by developing a highly efficient C++ implementation and deploying the resulting trajectories on a real industrial magnetic levitation platform.
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