Multi-Agent Motion Planning on Industrial Magnetic Levitation Platforms: A Hybrid ADMM-HOCBF approach
This addresses motion planning for multi-agent systems in industrial settings, offering a scalable solution with safety guarantees.
The paper tackles the intractability of classical centralised MPC for multi-agent motion planning by proposing a decentralised hybrid ADMM-HOCBF method, achieving significant scalability improvements validated on a real industrial magnetic levitation platform.
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.