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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.00352 |
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| _version_ | 1866915267994451968 |
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| author | Liang, Kaier Yang, Guang Cai, Mingyu Vasile, Cristian-Ioan |
| author_facet | Liang, Kaier Yang, Guang Cai, Mingyu Vasile, Cristian-Ioan |
| contents | We propose a novel framework for safe navigation in dynamic environments by integrating Koopman operator theory with conformal prediction. Our approach leverages data-driven Koopman approximation to learn nonlinear dynamics and employs conformal prediction to quantify uncertainty, providing statistical guarantees on approximation errors. This uncertainty is effectively incorporated into a Model Predictive Controller (MPC) formulation through constraint tightening, ensuring robust safety guarantees. We implement a layered control architecture with a reference generator providing waypoints for safe navigation. The effectiveness of our methods is validated in simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_00352 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Safe Navigation in Dynamic Environments Using Data-Driven Koopman Operators and Conformal Prediction Liang, Kaier Yang, Guang Cai, Mingyu Vasile, Cristian-Ioan Robotics We propose a novel framework for safe navigation in dynamic environments by integrating Koopman operator theory with conformal prediction. Our approach leverages data-driven Koopman approximation to learn nonlinear dynamics and employs conformal prediction to quantify uncertainty, providing statistical guarantees on approximation errors. This uncertainty is effectively incorporated into a Model Predictive Controller (MPC) formulation through constraint tightening, ensuring robust safety guarantees. We implement a layered control architecture with a reference generator providing waypoints for safe navigation. The effectiveness of our methods is validated in simulation. |
| title | Safe Navigation in Dynamic Environments Using Data-Driven Koopman Operators and Conformal Prediction |
| topic | Robotics |
| url | https://arxiv.org/abs/2504.00352 |