Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning (2403.07129v2)

Published 11 Mar 2024 in cs.RO

Abstract: The interactive decision-making in multi-agent autonomous racing offers insights valuable beyond the domain of self-driving cars. Mapless online path planning is particularly of practical appeal but poses a challenge for safely overtaking opponents due to the limited planning horizon. To address this, we introduce RaceMOP, a novel method for mapless online path planning designed for multi-agent racing of F1TENTH cars. Unlike classical planners that rely on predefined racing lines, RaceMOP operates without a map, utilizing only local observations to execute high-speed overtaking maneuvers. Our approach combines an artificial potential field method as a base policy with residual policy learning to enable long-horizon planning. We advance the field by introducing a novel approach for policy fusion with the residual policy directly in probability space. Extensive experiments on twelve simulated racetracks validate that RaceMOP is capable of long-horizon decision-making with robust collision avoidance during overtaking maneuvers. RaceMOP demonstrates superior handling over existing mapless planners and generalizes to unknown racetracks, affirming its potential for broader applications in robotics. Our code is available at http://github.com/raphajaner/racemop.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (37)
  1. A. Wischnewski, M. Geisslinger, J. Betz, T. Betz, F. Fent, A. Heilmeier, L. Hermansdorfer, T. Herrmann, S. Huch, P. Karle et al., “Indy autonomous challenge-autonomous race cars at the handling limits,” in 12th International Munich Chassis Symposium 2021: chassis. tech plus.   Springer, 2022, pp. 163–182.
  2. J. Betz, H. Zheng, A. Liniger, U. Rosolia, P. Karle, M. Behl, V. Krovi, and R. Mangharam, “Autonomous vehicles on the edge: A survey on autonomous vehicle racing,” IEEE Open Journal of Intelligent Transportation Systems, vol. 3, pp. 458–488, 2022.
  3. V. Suresh Babu and M. Behl, “Threading the needle—overtaking framework for multi-agent autonomous racing,” SAE International Journal of Connected and Automated Vehicles, vol. 5, no. 1, 2022.
  4. T. Stahl, A. Wischnewski, J. Betz, and M. Lienkamp, “Multilayer graph-based trajectory planning for race vehicles in dynamic scenarios,” in Int. Conf. on Intelligent Transportation Systems.   IEEE, 2019.
  5. M. O’Kelly, H. Zheng, D. Karthik, and R. Mangharam, “F1tenth: An open-source evaluation environment for continuous control and reinforcement learning,” in NeurIPS 2019 Competition and Demonstration Track.   PMLR, 2020, pp. 77–89.
  6. N. Otterness, “The ”Disparity Extender” Algorithm, and F1/Tenth.” [Online]. Available: https://www.nathanotterness.com/2019/04/the-disparity-extender-algorithm-and.html
  7. V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller, “Playing atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602, 2013.
  8. E. Kaufmann, L. Bauersfeld, A. Loquercio, M. Müller, V. Koltun, and D. Scaramuzza, “Champion-level drone racing using deep reinforcement learning,” Nature, vol. 620, no. 7976, pp. 982–987, 2023.
  9. P. Lin, W. Y. Choi, and C. C. Chung, “Local path planning using artificial potential field for waypoint tracking with collision avoidance,” in Int. Conf. on Intelligent Transportation Systems, 2020, pp. 1–7.
  10. R. Zhang, J. Hou, G. Chen, Z. Li, J. Chen, and A. Knoll, “Residual policy learning facilitates efficient model-free autonomous racing,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 11 625–11 632, 2022.
  11. R. Trumpp, D. Hoornaert, and M. Caccamo, “Residual policy learning for vehicle control of autonomous racing cars,” in IEEE Intelligent Vehicles Symposium.   IEEE, 2023, pp. 1–6.
  12. B. Evans, H. A. Engelbrecht, and H. W. Jordaan, “Learning the subsystem of local planning for autonomous racing,” in 2021 20th International Conference on Advanced Robotics (ICAR).   IEEE, 2021, pp. 601–606.
  13. T. Silver, K. Allen, J. Tenenbaum, and L. Kaelbling, “Residual policy learning,” arXiv preprint arXiv:1812.06298, 2018.
  14. A. Raji, A. Liniger, A. Giove, A. Toschi, N. Musiu, D. Morra, M. Verucchi, D. Caporale, and M. Bertogna, “Motion planning and control for multi vehicle autonomous racing at high speeds,” in Int. Conf. on Intelligent Transportation Systems.   IEEE, 2022.
  15. N. Li, E. Goubault, L. Pautet, and S. Putot, “A real-time nmpc controller for autonomous vehicle racing,” in 2022 6th International Conference on Automation, Control and Robots (ICACR).   IEEE, 2022, pp. 148–155.
  16. V. Sezer and M. Gokasan, “A novel obstacle avoidance algorithm: “follow the gap method”,” Robotics and Autonomous Systems, vol. 60, no. 9, pp. 1123–1134, 2012.
  17. S. Karaman and E. Frazzoli, “Incremental sampling-based algorithms for optimal motion planning,” Robotics Science and Systems VI, vol. 104, no. 2, pp. 267–274, 2010.
  18. S. Feraco, S. Luciani, A. Bonfitto, N. Amati, and A. Tonoli, “A local trajectory planning and control method for autonomous vehicles based on the rrt algorithm,” in AEIT international conference of electrical and electronic technologies for automotive.   IEEE, 2020, pp. 1–6.
  19. L. Ma, J. Xue, K. Kawabata, J. Zhu, C. Ma, and N. Zheng, “A fast rrt algorithm for motion planning of autonomous road vehicles,” in Int. Conf. on Intelligent Transportation Systems.   IEEE, 2014, pp. 1033–1038.
  20. F. Von Hundelshausen, M. Himmelsbach, F. Hecker, A. Mueller, and H.-J. Wuensche, “Driving with tentacles: Integral structures for sensing and motion,” Journal of Field Robotics, vol. 25, no. 9, 2008.
  21. C. Alia, T. Gilles, T. Reine, and C. Ali, “Local trajectory planning and tracking of autonomous vehicles, using clothoid tentacles method,” in IEEE Intelligent Vehicles Symposium.   IEEE, 2015, pp. 674–679.
  22. S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann et al., “Stanley: The robot that won the darpa grand challenge,” Journal of Field Robotics, vol. 23, no. 9, pp. 661–692, 2006.
  23. T. Uusitalo and S. J. Johansson, “A reactive mutli-agent approach to car driving using artificial potential fields,” in 2011 IEEE Conference on Computational Intelligence and Games (CIG’11).   IEEE, 2011, pp. 203–210.
  24. D. Loiacono, A. Prete, P. L. Lanzi, and L. Cardamone, “Learning to overtake in torcs using simple reinforcement learning,” in IEEE Congress on Evolutionary Computation.   IEEE, 2010, pp. 1–8.
  25. F. Fuchs, Y. Song, E. Kaufmann, D. Scaramuzza, and P. Dürr, “Super-human performance in gran turismo sport using deep reinforcement learning,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 4257–4264, 2021.
  26. Y. Song, H. Lin, E. Kaufmann, P. Dürr, and D. Scaramuzza, “Autonomous overtaking in gran turismo sport using curriculum reinforcement learning,” in Int. Conf. on Robotics and Automation.   IEEE, 2021, pp. 9403–9409.
  27. J. Zhang and H.-W. Loidl, “F1tenth: An over-taking algorithm using machine learning,” in 2023 28th International Conference on Automation and Computing (ICAC).   IEEE, 2023, pp. 01–06.
  28. P. Werner, T. Seyde, P. Drews, T. M. Balch, I. Gilitschenski, W. Schwarting, G. Rosman, S. Karaman, and D. Rus, “Dynamic multi-team racing: Competitive driving on 1/10-th scale vehicles via learning in simulation,” in 7th Annual Conference on Robot Learning, 2023.
  29. W. Schwarting, T. Seyde, I. Gilitschenski, L. Liebenwein, R. Sander, S. Karaman, and D. Rus, “Deep latent competition: Learning to race using visual control policies in latent space,” arXiv preprint arXiv:2102.09812, 2021.
  30. D. Kalaria, Q. Lin, and J. M. Dolan, “Towards optimal head-to-head autonomous racing with curriculum reinforcement learning,” arXiv preprint arXiv:2308.13491, 2023.
  31. M. G. Park, J. H. Jeon, and M. C. Lee, “Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing,” in IEEE International Symposium on Industrial Electronics Proceedings, vol. 3.   IEEE, 2001, pp. 1530–1535.
  32. O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” in Int. Conf. on Robotics and Automation, vol. 2, 1985, pp. 500–505.
  33. J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347, 2017.
  34. B. D. Evans, H. W. Jordaan, and H. A. Engelbrecht, “Safe reinforcement learning for high-speed autonomous racing,” Cognitive Robotics, vol. 3, pp. 107–126, 2023.
  35. J. Burkardt, “The truncated normal distribution,” Department of Scientific Computing Website, Florida State University, vol. 1, p. 35, 2014.
  36. M. M. Rahman and Y. Xue, “Robust policy optimization in deep reinforcement learning,” arXiv preprint arXiv:2212.07536, 2022.
  37. R. Trumpp, M. Büchner, A. Valada, and M. Caccamo, “Efficient learning of urban driving policies using bird’s-eye-view state representations,” in Int. Conf. on Intelligent Transportation Systems, 2023, pp. 4181–4186.
Citations (1)
View on Semantic Scholar

Summary

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

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