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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.19838 |
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| _version_ | 1866912975935242240 |
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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 |