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

2026-03-20

AI Summary

This work addresses the computational intractability of centralized model predictive control (MPC) in large-scale multi-agent industrial maglev platforms by proposing a hybrid motion planning framework that integrates decentralized alternating direction method of multipliers (ADMM) with centralized high-order control barrier functions (HOCBFs). The approach leverages decentralized MPC for efficient trajectory optimization while enforcing rigorous safety guarantees through a centralized HOCBF-based verification layer. By innovatively combining distributed computation with centralized safety certification, the method achieves both real-time performance and enhanced scalability. Experimental results from a C++ implementation demonstrate that, compared to conventional centralized MPC, the proposed strategy consistently delivers superior scalability, safety assurance, and real-time execution in both simulated and physical maglev platform environments.

Technology Category

• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements

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.

Problem

• multi-agent motion planning
• scalability
• safety guarantees
• industrial magnetic levitation
• centralised MPC

Innovation

• Hybrid ADMM-HOCBF
• Decentralized MPC
• Multi-Agent Motion Planning
• Scalability
• Safety Guarantees