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.

翻译：本文提出了一种用于全向多智能体系统的新型混合运动规划方法。所提出的分散模型预测控制（MPC）框架解决了经典集中式MPC在智能体数量增加时存在的计算棘手性问题，同时提供了安全保障。该方法通过将分散式的交替方向乘子法（ADMM）与集中式高阶控制屏障函数（HOCBF）架构相结合来实现。仿真结果表明，与经典集中式MPC相比，该方法在可扩展性方面有显著提升。我们通过开发高效的C++实现并将生成的轨迹部署在真实工业磁悬浮平台上，验证了所提出方法的有效性及实时处理能力。