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Bibliographic Details
Main Authors: Tistaert, Bavo, Servaes, Stan, Gonzalez-Garcia, Alejandro, Ibrahim, Ibrahim, Callens, Louis, Swevers, Jan, Decré, Wilm
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2603.19838
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author Tistaert, Bavo
Servaes, Stan
Gonzalez-Garcia, Alejandro
Ibrahim, Ibrahim
Callens, Louis
Swevers, Jan
Decré, Wilm
author_facet Tistaert, Bavo
Servaes, Stan
Gonzalez-Garcia, Alejandro
Ibrahim, Ibrahim
Callens, Louis
Swevers, Jan
Decré, Wilm
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2603_19838
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Multi-Agent Motion Planning on Industrial Magnetic Levitation Platforms: A Hybrid ADMM-HOCBF approach
Tistaert, Bavo
Servaes, Stan
Gonzalez-Garcia, Alejandro
Ibrahim, Ibrahim
Callens, Louis
Swevers, Jan
Decré, Wilm
Robotics
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.
title Multi-Agent Motion Planning on Industrial Magnetic Levitation Platforms: A Hybrid ADMM-HOCBF approach
topic Robotics
url https://arxiv.org/abs/2603.19838