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SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning (2209.11789v2)

Published 23 Sep 2022 in cs.RO and cs.AI

Abstract: Collision avoidance is key for mobile robots and agents to operate safely in the real world. In this work we present SAFER, an efficient and effective collision avoidance system that is able to improve safety by correcting the control commands sent by an operator. It combines real-world reinforcement learning (RL), search-based online trajectory planning, and automatic emergency intervention, e.g. automatic emergency braking (AEB). The goal of the RL is to learn an effective corrective control action that is used in a focused search for collision-free trajectories, and to reduce the frequency of triggering automatic emergency braking. This novel setup enables the RL policy to learn safely and directly on mobile robots in a real-world indoor environment, minimizing actual crashes even during training. Our real-world experiments show that, when compared with several baselines, our approach enjoys a higher average speed, lower crash rate, less emergency intervention, smaller computation overhead, and smoother overall control.

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References (43)
  1. Constrained policy optimization. In International conference on machine learning, pages 22–31. PMLR, 2017.
  2. Safe reinforcement learning via shielding. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32, 2018.
  3. Safe reinforcement learning via statistical model predictive shielding. In Robotics: Science and Systems, pages 1–13, 2021.
  4. Control synthesis from linear temporal logic specifications using model-free reinforcement learning. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 10349–10355. IEEE, 2020.
  5. Dynamic channel: A planning framework for crowd navigation. In 2019 International Conference on Robotics and Automation (ICRA), pages 5551–5557, 2019.
  6. Reinforcement based mobile robot path planning with improved dynamic window approach in unknown environment. Autonomous Robots, 45:51–76, 2021.
  7. End-to-end driving via conditional imitation learning. In 2018 IEEE international conference on robotics and automation (ICRA), pages 4693–4700. IEEE, 2018.
  8. Real-time model predictive control for keeping a quadrotor visible on the camera field-of-view of a ground robot. In 2016 American Control Conference (ACC), pages 2259–2264, 2016.
  9. Adaptive dynamic window approach for local navigation. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6930–6936. IEEE, 2020.
  10. Safe multi-agent reinforcement learning via shielding. arXiv preprint arXiv:2101.11196, 2021.
  11. The dynamic window approach to collision avoidance. IEEE Robotics & Automation Magazine, 4(1):23–33, 1997.
  12. Learning to fly by crashing. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3948–3955. IEEE, 2017.
  13. A machine learning approach to visual perception of forest trails for mobile robots. IEEE Robotics and Automation Letters, 1(2):661–667, 2015.
  14. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In International conference on machine learning, pages 1861–1870. PMLR, 2018.
  15. Reinforcement learning for temporal logic control synthesis with probabilistic satisfaction guarantees. In 2019 IEEE 58th conference on decision and control (CDC), pages 5338–5343. IEEE, 2019.
  16. Dynamic window approach with path-following for unmanned surface vehicle based on reinforcement learning. Journal of the Korea Institute of Military Science and Technology, 24(1):61–69, 2021.
  17. Deep visual mpc-policy learning for navigation. IEEE Robotics and Automation Letters, 4(4):3184–3191, 2019.
  18. Autonomous emergency braking test results. In Proceedings of the 23rd International Technical Conference on the Enhanced Safety of Vehicles (ESV), pages 1–13. National Highway Traffic Safety Administration Washington, DC, 2013.
  19. Badgr: An autonomous self-supervised learning-based navigation system. IEEE Robotics and Automation Letters, 6(2):1312–1319, 2021.
  20. Land: Learning to navigate from disengagements. IEEE Robotics and Automation Letters, 6(2):1872–1879, 2021.
  21. Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 5129–5136. IEEE, 2018.
  22. Uncertainty-aware reinforcement learning for collision avoidance. arXiv preprint arXiv:1702.01182, 2017.
  23. Improvement of dynamic window approach using reinforcement learning in dynamic environments. International Journal of Control, Automation and Systems, 20(9):2983–2992, 2022.
  24. A survey of generalisation in deep reinforcement learning. arXiv preprint arXiv:2111.09794, 2021.
  25. Steven M LaValle. Planning algorithms, 2006.
  26. Lifelong planning for mobile robots. In Advances in Plan-Based Control of Robotic Agents, pages 140–156. Springer, 2002.
  27. Dronet: Learning to fly by driving. IEEE Robotics and Automation Letters, 3(2):1088–1095, 2018.
  28. Driving policy transfer via modularity and abstraction. arXiv preprint arXiv:1804.09364, 2018.
  29. Off-road obstacle avoidance through end-to-end learning. Advances in neural information processing systems, 18, 2005.
  30. Rrtx: Asymptotically optimal single-query sampling-based motion planning with quick replanning. The International Journal of Robotics Research, 35(7):797–822, 2016.
  31. Agile autonomous driving using end-to-end deep imitation learning. arXiv preprint arXiv:1709.07174, 2017.
  32. Dynamically feasible deep reinforcement learning policy for robot navigation in dense mobile crowds. arXiv preprint arXiv:2010.14838, 2020.
  33. Erik Rosen. Autonomous emergency braking for vulnerable road users. In IRCOBI conference, volume 2013, pages 618–627, 2013.
  34. Integrated online trajectory planning and optimization in distinctive topologies. Robotics and Autonomous Systems, 88:142–153, 2017.
  35. Learning monocular reactive uav control in cluttered natural environments. In 2013 IEEE international conference on robotics and automation, pages 1765–1772. IEEE, 2013.
  36. Cad2rl: Real single-image flight without a single real image. arXiv preprint arXiv:1611.04201, 2016.
  37. Resolution-adaptive risk-aware trajectory planning for surface vehicles operating in congested civilian traffic. Autonomous Robots, 40(7):1139–1163, 2016.
  38. Target following with motion prediction for unmanned surface vehicle operating in cluttered environments. Autonomous Robots, 36(4):383–405, 2014.
  39. Reciprocal velocity obstacles for real-time multi-agent navigation. In 2008 IEEE international conference on robotics and automation, pages 1928–1935. Ieee, 2008.
  40. Vision-based mobile robotics obstacle avoidance with deep reinforcement learning. In 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 14360–14366. IEEE, 2021.
  41. Optimization-based collision avoidance. IEEE Transactions on Control Systems Technology, 29(3):972–983, 2021.
  42. Sim-to-real transfer in deep reinforcement learning for robotics: a survey. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pages 737–744. IEEE, 2020.
  43. Learning interaction-aware trajectory predictions for decentralized multi-robot motion planning in dynamic environments. IEEE Robotics and Automation Letters, 6(2):2256–2263, 2021.
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