Multi-Agent Motion Planning on Industrial Magnetic Levitation Platforms: A Hybrid ADMM-HOCBF approach

TL;DR

This paper introduces a hybrid decentralized motion planning approach combining ADMM and HOCBF for multi-agent systems, demonstrating improved scalability and real-time performance on industrial magnetic levitation platforms.

Contribution

It proposes a novel hybrid ADMM-HOCBF framework for scalable, safe, and real-time multi-agent motion planning, validated on real industrial hardware.

Findings

Significant scalability improvement over classical centralized MPC.

Successful real-time deployment on industrial magnetic levitation platforms.

Efficient C++ implementation demonstrating practical applicability.

Abstract

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.