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Robo-centric ESDF: A Fast and Accurate Whole-body Collision Evaluation Tool for Any-shape Robotic Planning (2306.16046v1)

Published 28 Jun 2023 in cs.RO

Abstract: For letting mobile robots travel flexibly through complicated environments, increasing attention has been paid to the whole-body collision evaluation. Most existing works either opt for the conservative corridor-based methods that impose strict requirements on the corridor generation, or ESDF-based methods that suffer from high computational overhead. It is still a great challenge to achieve fast and accurate whole-body collision evaluation. In this paper, we propose a Robo-centric ESDF (RC-ESDF) that is pre-built in the robot body frame and is capable of seamlessly applied to any-shape mobile robots, even for those with non-convex shapes. RC-ESDF enjoys lazy collision evaluation, which retains only the minimum information sufficient for whole-body safety constraint and significantly speeds up trajectory optimization. Based on the analytical gradients provided by RC-ESDF, we optimize the position and rotation of robot jointly, with whole-body safety, smoothness, and dynamical feasibility taken into account. Extensive simulation and real-world experiments verified the reliability and generalizability of our method.

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References (20)
  1. X. Zhou, Z. Wang, H. Ye, C. Xu, and F. Gao, “Ego-planner: An esdf-free gradient-based local planner for quadrotors,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 478–485, 2021.
  2. B. Zhou, F. Gao, L. Wang, C. Liu, and S. Shen, “Robust and efficient quadrotor trajectory generation for fast autonomous flight,” IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 3529–3536, 2019.
  3. F. Gao, L. Wang, B. Zhou, X. Zhou, J. Pan, and S. Shen, “Teach-repeat-replan: A complete and robust system for aggressive flight in complex environments,” IEEE Transactions on Robotics, vol. 36, no. 5, pp. 1526–1545, 2020.
  4. J. Ji, Z. Wang, Y. Wang, C. Xu, and F. Gao, “Mapless-planner: A robust and fast planning framework for aggressive autonomous flight without map fusion,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 6315–6321.
  5. S. Liu, K. Mohta, N. Atanasov, and V. Kumar, “Search-based motion planning for aggressive flight in se (3),” IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 2439–2446, 2018.
  6. X. Zhang, A. Liniger, A. Sakai, and F. Borrelli, “Autonomous parking using optimization-based collision avoidance,” in 2018 IEEE Conference on Decision and Control (CDC).   IEEE, 2018, pp. 4327–4332.
  7. R. He, J. Zhou, S. Jiang, Y. Wang, J. Tao, S. Song, J. Hu, J. Miao, and Q. Luo, “Tdr-obca: A reliable planner for autonomous driving in free-space environment,” in 2021 American Control Conference (ACC).   IEEE, 2021, pp. 2927–2934.
  8. Z. Han, Z. Wang, N. Pan, Y. Lin, C. Xu, and F. Gao, “Fast-racing: An open-source strong baseline for se(3) planning in autonomous drone racing,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 8631–8638, 2021.
  9. W. Ding, L. Zhang, J. Chen, and S. Shen, “Safe trajectory generation for complex urban environments using spatio-temporal semantic corridor,” IEEE Robotics and Automation Letters, vol. 4, no. 3, pp. 2997–3004, 2019.
  10. S. Manzinger, C. Pek, and M. Althoff, “Using reachable sets for trajectory planning of automated vehicles,” IEEE Transactions on Intelligent Vehicles, vol. 6, no. 2, pp. 232–248, 2020.
  11. B. Li, T. Acarman, Y. Zhang, Y. Ouyang, C. Yaman, Q. Kong, X. Zhong, and X. Peng, “Optimization-based trajectory planning for autonomous parking with irregularly placed obstacles: A lightweight iterative framework,” IEEE Transactions on Intelligent Transportation Systems, 2021.
  12. J. Ji, T. Yang, C. Xu, and F. Gao, “Real-time trajectory planning for aerial perching,” arXiv preprint arXiv:2203.01061, 2022.
  13. E. G. Gilbert, D. W. Johnson, and S. S. Keerthi, “A fast procedure for computing the distance between complex objects in three-dimensional space,” IEEE Journal on Robotics and Automation, vol. 4, no. 2, pp. 193–203, 1988.
  14. S. Cameron, “Enhancing gjk: Computing minimum and penetration distances between convex polyhedra,” in Proceedings of international conference on robotics and automation, vol. 4.   IEEE, 1997, pp. 3112–3117.
  15. Z. Wang, X. Zhou, C. Xu, and F. Gao, “Geometrically constrained trajectory optimization for multicopters,” IEEE Transactions on Robotics, 2022.
  16. Z. Zhu, E. Schmerling, and M. Pavone, “A convex optimization approach to smooth trajectories for motion planning with car-like robots,” in 2015 54th IEEE conference on decision and control (CDC).   IEEE, 2015, pp. 835–842.
  17. B. Li, Y. Ouyang, Y. Zhang, T. Acarman, Q. Kong, and Z. Shao, “Optimal cooperative maneuver planning for multiple nonholonomic robots in a tiny environment via adaptive-scaling constrained optimization,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1511–1518, 2021.
  18. P. F. Felzenszwalb and D. P. Huttenlocher, “Distance transforms of sampled functions,” Theory of computing, vol. 8, no. 1, pp. 415–428, 2012.
  19. D. C. Liu and J. Nocedal, “On the limited memory bfgs method for large scale optimization,” Mathematical programming, vol. 45, no. 1, pp. 503–528, 1989.
  20. A. S. Lewis and M. L. Overton, “Nonsmooth optimization via quasi-newton methods,” Mathematical Programming, vol. 141, no. 1, pp. 135–163, 2013.
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