An MPC framework for efficient navigation of mobile robots in cluttered environments
Fuente:
arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915892043972608 |
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| author | Köhler, Johannes Zhang, Daniel Soloperto, Raffaele Carron, Andrea Zeilinger, Melanie |
| author_facet | Köhler, Johannes Zhang, Daniel Soloperto, Raffaele Carron, Andrea Zeilinger, Melanie |
| contents | We present a model predictive control (MPC) framework for efficient navigation of mobile robots in cluttered environments. The proposed approach integrates a finite-segment shortest path planner into the finite-horizon trajectory optimization of the MPC. This formulation ensures convergence to dynamically selected targets and guarantees collision avoidance, even under general nonlinear dynamics and cluttered environments. The approach is validated through hardware experiments on a small ground robot, where a human operator dynamically assigns target locations that a robot should reach while avoiding obstacles. The robot reached new targets within 2-3 seconds and responded to new commands within 50 ms to 100 ms, immediately adjusting its motion even while still moving at high speeds toward a previous target. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15917 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An MPC framework for efficient navigation of mobile robots in cluttered environments Köhler, Johannes Zhang, Daniel Soloperto, Raffaele Carron, Andrea Zeilinger, Melanie Robotics Systems and Control Optimization and Control We present a model predictive control (MPC) framework for efficient navigation of mobile robots in cluttered environments. The proposed approach integrates a finite-segment shortest path planner into the finite-horizon trajectory optimization of the MPC. This formulation ensures convergence to dynamically selected targets and guarantees collision avoidance, even under general nonlinear dynamics and cluttered environments. The approach is validated through hardware experiments on a small ground robot, where a human operator dynamically assigns target locations that a robot should reach while avoiding obstacles. The robot reached new targets within 2-3 seconds and responded to new commands within 50 ms to 100 ms, immediately adjusting its motion even while still moving at high speeds toward a previous target. |
| title | An MPC framework for efficient navigation of mobile robots in cluttered environments |
| topic | Robotics Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2509.15917 |