Parameterized Fast and Safe Tracking (FaSTrack) using Deepreach (2404.07431v1)
Abstract: Fast and Safe Tracking (FaSTrack) is a modular framework that provides safety guarantees while planning and executing trajectories in real time via value functions of Hamilton-Jacobi (HJ) reachability. These value functions are computed through dynamic programming, which is notorious for being computationally inefficient. Moreover, the resulting trajectory does not adapt online to the environment, such as sudden disturbances or obstacles. DeepReach is a scalable deep learning method to HJ reachability that allows parameterization of states, which opens up possibilities for online adaptation to various controls and disturbances. In this paper, we propose Parametric FaSTrack, which uses DeepReach to approximate a value function that parameterizes the control bounds of the planning model. The new framework can smoothly trade off between the navigation speed and the tracking error (therefore maneuverability) while guaranteeing obstacle avoidance in a priori unknown environments. We demonstrate our method through two examples and a benchmark comparison with existing methods, showing the safety, efficiency, and faster solution times of the framework.
- Adaptive sampling-based motion planning with control barrier functions, 2022.
- Robust model predictive flight control of unmanned rotorcrafts. J. Intelligent & Robotic Systems, 81(3-4):443–469, 2016.
- DeepReach: A deep learning approach to high-dimensional reachability. In IEEE International Conference on Robotics and Automation (ICRA), 2021.
- Hamilton-jacobi reachability: A brief overview and recent advances. In 2017 IEEE 56th Annual Conference on Decision and Control (CDC), pages 2242–2253. IEEE, 2017.
- Parameter-conditioned reachable sets for updating safety assurances online. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 10553–10559, 2023. 10.1109/ICRA48891.2023.10160554.
- Robust control barrier-value functions for safety-critical control, 2021.
- Reach-avoid problems with time-varying dynamics, targets and constraints. In Proceedings of the 18th international conference on hybrid systems: computation and control, pages 11–20, 2015.
- Probabilistically safe robot planning with confidence-based human predictions. arXiv preprint arXiv:1806.00109, 2018.
- Planning, fast and slow: A framework for adaptive real-time safe trajectory planning. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 387–394. IEEE, 2018.
- Fastrack: a modular framework for fast and guaranteed safe motion planning. IEEE Conference on Decision and Control, 2017. URL https://arxiv.org/pdf/1703.07373.pdf.
- Ensuring safety for vehicle parking tasks using hamilton-jacobi reachability analysis. In 2020 59th IEEE Conference on Decision and Control (CDC), pages 1416–1421, 2020. 10.1109/CDC42340.2020.9304186.
- M. Kobilarov. Cross-entropy motion planning. Int. J. Robotics Research, 31(7):855–871, 2012.
- Generating formal safety assurances for high-dimensional reachability. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 10525–10531, 2023. 10.1109/ICRA48891.2023.10160600.
- Safe and robust motion planning for dynamic robotics via control barrier functions, 2021.
- Online update of safety assurances using confidence-based predictions. In 2023 IEEE International Conference on Robotics and Automation (ICRA), pages 12765–12771, 2023. 10.1109/ICRA48891.2023.10160828.
- A survey of industrial model predictive control technology. Control Engineering Practice, 11(7):733–764, 2003.
- A classification-based approach for approximate reachability. In 2019 International Conference on Robotics and Automation (ICRA), pages 7697–7704, 2019. 10.1109/ICRA.2019.8793919.
- Atefeh Sahraeekhanghah and Mo Chen. Pa-fastrack: Planner-aware real-time guaranteed safe planning. In 2021 60th IEEE Conference on Decision and Control (CDC), pages 2129–2136. IEEE, 2021.
- Finding locally optimal, collision-free trajectories with sequential convex optimization. In Proc. Robotics: Science and Systems, 2013.
- Real-time robust receding horizon planning using hamilton–jacobi reachability analysis. IEEE Transactions on Robotics, 39(1):90–109, 2023. 10.1109/TRO.2022.3187291.
- Refining control barrier functions through hamilton-jacobi reachability, 2022.
- Aggressive driving with model predictive path integral control. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages 1433–1440, 2016. 10.1109/ICRA.2016.7487277.
- Information-theoretic model predictive control: Theory and applications to autonomous driving. IEEE Transactions on Robotics, 34(6):1603–1622, 2018. 10.1109/TRO.2018.2865891.