Safety on the Fly: Constructing Robust Safety Filters via Policy Control Barrier Functions at Runtime
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911385272713216 |
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| author | Knoedler, Luzia So, Oswin Yin, Ji Black, Mitchell Serlin, Zachary Tsiotras, Panagiotis Alonso-Mora, Javier Fan, Chuchu |
| author_facet | Knoedler, Luzia So, Oswin Yin, Ji Black, Mitchell Serlin, Zachary Tsiotras, Panagiotis Alonso-Mora, Javier Fan, Chuchu |
| contents | Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the Robust Policy CBF (RPCBF), a practical approach for constructing robust CBF approximations online via the estimation of a value function. We establish conditions under which the approximation qualifies as a valid CBF and demonstrate the effectiveness of the RPCBF-safety filter in simulation on a variety of high relative degree input-constrained systems. Finally, we demonstrate the benefits of our method in compensating for model errors on a hardware quadcopter platform by treating the model errors as disturbances. Website including code: www.oswinso.xyz/rpcbf/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11157 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Safety on the Fly: Constructing Robust Safety Filters via Policy Control Barrier Functions at Runtime Knoedler, Luzia So, Oswin Yin, Ji Black, Mitchell Serlin, Zachary Tsiotras, Panagiotis Alonso-Mora, Javier Fan, Chuchu Optimization and Control Robotics Control Barrier Functions (CBFs) have proven to be an effective tool for performing safe control synthesis for nonlinear systems. However, guaranteeing safety in the presence of disturbances and input constraints for high relative degree systems is a difficult problem. In this work, we propose the Robust Policy CBF (RPCBF), a practical approach for constructing robust CBF approximations online via the estimation of a value function. We establish conditions under which the approximation qualifies as a valid CBF and demonstrate the effectiveness of the RPCBF-safety filter in simulation on a variety of high relative degree input-constrained systems. Finally, we demonstrate the benefits of our method in compensating for model errors on a hardware quadcopter platform by treating the model errors as disturbances. Website including code: www.oswinso.xyz/rpcbf/ |
| title | Safety on the Fly: Constructing Robust Safety Filters via Policy Control Barrier Functions at Runtime |
| topic | Optimization and Control Robotics |
| url | https://arxiv.org/abs/2410.11157 |