Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
162 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A GP-based Robust Motion Planning Framework for Agile Autonomous Robot Navigation and Recovery in Unknown Environments (2402.01617v1)

Published 2 Feb 2024 in cs.RO and cs.LG

Abstract: For autonomous mobile robots, uncertainties in the environment and system model can lead to failure in the motion planning pipeline, resulting in potential collisions. In order to achieve a high level of robust autonomy, these robots should be able to proactively predict and recover from such failures. To this end, we propose a Gaussian Process (GP) based model for proactively detecting the risk of future motion planning failure. When this risk exceeds a certain threshold, a recovery behavior is triggered that leverages the same GP model to find a safe state from which the robot may continue towards the goal. The proposed approach is trained in simulation only and can generalize to real world environments on different robotic platforms. Simulations and physical experiments demonstrate that our framework is capable of both predicting planner failures and recovering the robot to states where planner success is likely, all while producing agile motion.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (20)
  1. X. Xiao, Z. Xu, Z. Wang, Y. Song, G. Warnell, P. Stone, T. Zhang, S. Ravi, G. Wang, H. Karnan, J. Biswas, N. Mohammad, L. Bramblett, R. Peddi, N. Bezzo, Z. Xie, and P. Dames, “Autonomous ground navigation in highly constrained spaces: Lessons learned from the benchmark autonomous robot navigation challenge at icra 2022 [competitions],” IEEE Robotics & Automation Magazine, vol. 29, no. 4, pp. 148–156, 2022.
  2. N. Mohammad and N. Bezzo, “A robust and fast occlusion-based frontier method for autonomous navigation in unknown cluttered environments,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 6324–6331.
  3. X. Zhang, Y. Shu, Y. Chen, G. Chen, J. Ye, X. Li, and X. Li, “Multi-modal learning and relaxation of physical conflict for an exoskeleton robot with proprioceptive perception,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 10 490–10 496.
  4. T. Ji, A. N. Sivakumar, G. Chowdhary, and K. Driggs-Campbell, “Proactive anomaly detection for robot navigation with multi-sensor fusion,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 4975–4982, 2022.
  5. G. Kahn, P. Abbeel, and S. Levine, “Badgr: An autonomous self-supervised learning-based navigation system,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1312–1319, 2021.
  6. S. Liu, M. Watterson, K. Mohta, K. Sun, S. Bhattacharya, C. J. Taylor, and V. Kumar, “Planning dynamically feasible trajectories for quadrotors using safe flight corridors in 3-d complex environments,” IEEE Robotics and Automation Letters, vol. 2, no. 3, pp. 1688–1695, 2017.
  7. F. Gao, W. Wu, Y. Lin, and S. Shen, “Online safe trajectory generation for quadrotors using fast marching method and bernstein basis polynomial,” in 2018 IEEE International Conference on Robotics and Automation (ICRA), 2018, pp. 344–351.
  8. L. Wang and Y. Guo, “Speed adaptive robot trajectory generation based on derivative property of b-spline curve,” IEEE Robotics and Automation Letters, vol. 8, no. 4, pp. 1905–1911, 2023.
  9. J. Tordesillas and J. P. How, “FASTER: Fast and safe trajectory planner for navigation in unknown environments,” IEEE Transactions on Robotics, 2021.
  10. Z. Wang, X. Zhou, C. Xu, and F. Gao, “Geometrically constrained trajectory optimization for multicopters,” IEEE Transactions on Robotics, vol. 38, no. 5, pp. 3259–3278, 2022.
  11. Y. Ren, F. Zhu, W. Liu, Z. Wang, Y. Lin, F. Gao, and F. Zhang, “Bubble planner: Planning high-speed smooth quadrotor trajectories using receding corridors,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 6332–6339.
  12. S. Bansal, M. Chen, S. Herbert, and C. J. Tomlin, “Hamilton-jacobi reachability: A brief overview and recent advances,” in 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017, pp. 2242–2253.
  13. A. Devonport and M. Arcak, “Data-driven reachable set computation using adaptive gaussian process classification and monte carlo methods,” in 2020 American Control Conference (ACC), 2020, pp. 2629–2634.
  14. E. Yel, T. J. Carpenter, C. Di Franco, R. Ivanov, Y. Kantaros, I. Lee, J. Weimer, and N. Bezzo, “Assured runtime monitoring and planning: Toward verification of neural networks for safe autonomous operations,” IEEE Robotics & Automation Magazine, vol. 27, no. 2, pp. 102–116, 2020.
  15. D. Harabor and A. Grastien, “Online graph pruning for pathfinding on grid maps,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 25, no. 1, 2011, pp. 1114–1119.
  16. J. Tordesillas and J. P. How, “Minvo basis: Finding simplexes with minimum volume enclosing polynomial curves,” Computer-Aided Design, vol. 151, p. 103341, 2022.
  17. H. Oleynikova, M. Burri, Z. Taylor, J. Nieto, R. Siegwart, and E. Galceran, “Continuous-time trajectory optimization for online uav replanning,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016.
  18. A. Majumdar and M. Pavone, “How should a robot assess risk? towards an axiomatic theory of risk in robotics,” in Robotics Research: The 18th International Symposium ISRR.   Springer, 2020, pp. 75–84.
  19. G. Kahn, P. Abbeel, and S. Levine, “Land: Learning to navigate from disengagements,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1872–1879, 2021.
  20. R. Tedrake and the Drake Development Team, “Drake: Model-based design and verification for robotics,” 2019. [Online]. Available: https://drake.mit.edu

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now