Sensor-Based Distributionally Robust Control for Safe Robot Navigation in Dynamic Environments
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866918010095140864 |
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| author | Long, Kehan Yi, Yinzhuang Dai, Zhirui Herbert, Sylvia Cortés, Jorge Atanasov, Nikolay |
| author_facet | Long, Kehan Yi, Yinzhuang Dai, Zhirui Herbert, Sylvia Cortés, Jorge Atanasov, Nikolay |
| contents | We introduce a novel method for mobile robot navigation in dynamic, unknown environments, leveraging onboard sensing and distributionally robust optimization to impose probabilistic safety constraints. Our method introduces a distributionally robust control barrier function (DR-CBF) that directly integrates noisy sensor measurements and state estimates to define safety constraints. This approach is applicable to a wide range of control-affine dynamics, generalizable to robots with complex geometries, and capable of operating at real-time control frequencies. Coupled with a control Lyapunov function (CLF) for path following, the proposed CLF-DR-CBF control synthesis method achieves safe, robust, and efficient navigation in challenging environments. We demonstrate the effectiveness and robustness of our approach for safe autonomous navigation under uncertainty in simulations and real-world experiments with differential-drive robots. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18251 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Sensor-Based Distributionally Robust Control for Safe Robot Navigation in Dynamic Environments Long, Kehan Yi, Yinzhuang Dai, Zhirui Herbert, Sylvia Cortés, Jorge Atanasov, Nikolay Robotics Systems and Control Optimization and Control We introduce a novel method for mobile robot navigation in dynamic, unknown environments, leveraging onboard sensing and distributionally robust optimization to impose probabilistic safety constraints. Our method introduces a distributionally robust control barrier function (DR-CBF) that directly integrates noisy sensor measurements and state estimates to define safety constraints. This approach is applicable to a wide range of control-affine dynamics, generalizable to robots with complex geometries, and capable of operating at real-time control frequencies. Coupled with a control Lyapunov function (CLF) for path following, the proposed CLF-DR-CBF control synthesis method achieves safe, robust, and efficient navigation in challenging environments. We demonstrate the effectiveness and robustness of our approach for safe autonomous navigation under uncertainty in simulations and real-world experiments with differential-drive robots. |
| title | Sensor-Based Distributionally Robust Control for Safe Robot Navigation in Dynamic Environments |
| topic | Robotics Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2405.18251 |