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Learning to Model and Plan for Wheeled Mobility on Vertically Challenging Terrain (2306.11611v2)

Published 20 Jun 2023 in cs.RO

Abstract: Most autonomous navigation systems assume wheeled robots are rigid bodies and their 2D planar workspaces can be divided into free spaces and obstacles. However, recent wheeled mobility research, showing that wheeled platforms have the potential of moving over vertically challenging terrain (e.g., rocky outcroppings, rugged boulders, and fallen tree trunks), invalidate both assumptions. Navigating off-road vehicle chassis with long suspension travel and low tire pressure in places where the boundary between obstacles and free spaces is blurry requires precise 3D modeling of the interaction between the chassis and the terrain, which is complicated by suspension and tire deformation, varying tire-terrain friction, vehicle weight distribution and momentum, etc. In this paper, we present a learning approach to model wheeled mobility, i.e., in terms of vehicle-terrain forward dynamics, and plan feasible, stable, and efficient motion to drive over vertically challenging terrain without rolling over or getting stuck. We present physical experiments on two wheeled robots and show that planning using our learned model can achieve up to 60% improvement in navigation success rate and 46% reduction in unstable chassis roll and pitch angles.

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References (70)
  1. IEEE Spectrum, “Kiva systems – three engineers, hundreds of robots, one warehouse,” https://spectrum.ieee.org/three-engineers-hundreds-of-robots-one-warehouse, 2008, accessed: 2023-05-16.
  2. iRobot, “iRobot – robot vacuum and mop,” https://www.irobot.com/, 2023, accessed: 2023-05-16.
  3. Amazon, “Meet scout,” https://www.aboutamazon.com/news/transportation/meet-scout, 2023, accessed: 2023-05-16.
  4. D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” IEEE Robotics & Automation Magazine, vol. 4, no. 1, pp. 23–33, 1997.
  5. S. Quinlan and O. Khatib, “Elastic bands: Connecting path planning and control,” in [1993] Proceedings IEEE International Conference on Robotics and Automation.   IEEE, 1993, pp. 802–807.
  6. C. Rösmann, F. Hoffmann, and T. Bertram, “Kinodynamic trajectory optimization and control for car-like robots,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2017, pp. 5681–5686.
  7. X. Xiao, Z. Xu, Z. Wang, Y. Song, G. Warnell, P. Stone, T. Zhang, S. Ravi, G. Wang, H. Karnan et al., “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.
  8. D. Perille, A. Truong, X. Xiao, and P. Stone, “Benchmarking metric ground navigation,” in 2020 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).   IEEE, 2020, pp. 116–121.
  9. A. Nair, F. Jiang, K. Hou, Z. Xu, S. Li, X. Xiao, and P. Stone, “Dynabarn: Benchmarking metric ground navigation in dynamic environments,” in 2022 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).   IEEE, 2022, pp. 347–352.
  10. F. Cordes, C. Oekermann, A. Babu, D. Kuehn, T. Stark, F. Kirchner, and D. Bremen, “An active suspension system for a planetary rover,” in Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS), 2014, pp. 17–19.
  11. M. R. Islam, F. H. Chowdhury, S. Rezwan, M. J. Ishaque, J. U. Akanda, A. S. Tuhel, and B. B. Riddhe, “Novel design and performance analysis of a mars exploration robot: Mars rover mongol pothik,” in 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN).   IEEE, 2017, pp. 132–136.
  12. H. Jiang, G. Xu, W. Zeng, F. Gao, and K. Chong, “Lateral stability of a mobile robot utilizing an active adjustable suspension,” Applied Sciences, vol. 9, no. 20, p. 4410, 2019.
  13. Y. Liu and T. Seo, “Anyclimb-ii: Dry-adhesive linkage-type climbing robot for uneven vertical surfaces,” Mechanism and Machine Theory, vol. 124, pp. 197–210, 2018.
  14. X. Xiao and R. Murphy, “A review on snake robot testbeds in granular and restricted maneuverability spaces,” Robotics and Autonomous Systems, vol. 110, pp. 160–172, 2018.
  15. P. McGarey, F. Pomerleau, and T. D. Barfoot, “System design of a tethered robotic explorer (trex) for 3d mapping of steep terrain and harsh environments,” in Field and Service Robotics: Results of the 10th International Conference.   Springer, 2016, pp. 267–281.
  16. A. Datar, C. Pan, M. Nazeri, and X. Xiao, “Toward wheeled mobility on vertically challenging terrain: Platforms, datasets, and algorithms,” arXiv preprint arXiv:2303.00998, 2023.
  17. X. Xiao, E. Cappo, W. Zhen, J. Dai, K. Sun, C. Gong, M. J. Travers, and H. Choset, “Locomotive reduction for snake robots,” in 2015 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2015, pp. 3735–3740.
  18. R. Murphy, J. Dufek, T. Sarmiento, G. Wilde, X. Xiao, J. Braun, L. Mullen, R. Smith, S. Allred, J. Adams et al., “Two case studies and gaps analysis of flood assessment for emergency management with small unmanned aerial systems,” in 2016 IEEE international symposium on safety, security, and rescue robotics (SSRR).   IEEE, 2016, pp. 54–61.
  19. X. Xiao, J. Dufek, T. Woodbury, and R. Murphy, “Uav assisted usv visual navigation for marine mass casualty incident response,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2017, pp. 6105–6110.
  20. X. Xiao, J. Dufek, and R. R. Murphy, “Autonomous visual assistance for robot operations using a tethered uav,” in Field and Service Robotics: Results of the 12th International Conference.   Springer, 2021, pp. 15–29.
  21. G. Yan, M. Fang, and J. Xu, “Analysis and experiment of time-delayed optimal control for vehicle suspension system,” Journal of Sound and Vibration, vol. 446, pp. 144–158, 2019.
  22. A. A. Aly and F. A. Salem, “Vehicle suspension systems control: a review,” International journal of control, automation and systems, vol. 2, no. 2, pp. 46–54, 2013.
  23. P. Atreya, H. Karnan, K. S. Sikand, X. Xiao, S. Rabiee, and J. Biswas, “High-speed accurate robot control using learned forward kinodynamics and non-linear least squares optimization,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2022, pp. 11 789–11 795.
  24. X. Xiao, J. Biswas, and P. Stone, “Learning inverse kinodynamics for accurate high-speed off-road navigation on unstructured terrain,” IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 6054–6060, 2021.
  25. R. Manduchi, A. Castano, A. Talukder, and L. Matthies, “Obstacle detection and terrain classification for autonomous off-road navigation,” Autonomous robots, vol. 18, pp. 81–102, 2005.
  26. L. D. Jackel, E. Krotkov, M. Perschbacher, J. Pippine, and C. Sullivan, “The darpa lagr program: Goals, challenges, methodology, and phase i results,” Journal of Field robotics, vol. 23, no. 11-12, pp. 945–973, 2006.
  27. H. Mousazadeh, “A technical review on navigation systems of agricultural autonomous off-road vehicles,” Journal of Terramechanics, vol. 50, no. 3, pp. 211–232, 2013.
  28. R. Mirsky, X. Xiao, J. Hart, and P. Stone, “Conflict avoidance in social navigation–a survey,” arXiv preprint arXiv:2106.12113, 2021.
  29. H. Karnan, A. Nair, X. Xiao, G. Warnell, S. Pirk, A. Toshev, J. Hart, J. Biswas, and P. Stone, “Socially compliant navigation dataset (scand): A large-scale dataset of demonstrations for social navigation,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 11 807–11 814, 2022.
  30. Y. F. Chen, M. Everett, M. Liu, and J. P. How, “Socially aware motion planning with deep reinforcement learning,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2017, pp. 1343–1350.
  31. X. Xiao, T. Zhang, K. M. Choromanski, T.-W. E. Lee, A. Francis, J. Varley, S. Tu, S. Singh, P. Xu, F. Xia, S. M. Persson, L. Takayama, R. Frostig, J. Tan, C. Parada, and V. Sindhwani, “Learning model predictive controllers with real-time attention for real-world navigation,” in Conference on robot learning.   PMLR, 2022.
  32. A. Francis, C. Pérez-d’Arpino, C. Li, F. Xia, A. Alahi, R. Alami, A. Bera, A. Biswas, J. Biswas, R. Chandra et al., “Principles and guidelines for evaluating social robot navigation algorithms,” arXiv preprint arXiv:2306.16740, 2023.
  33. J.-S. Park, X. Xiao, G. Warnell, H. Yedidsion, and P. Stone, “Learning perceptual hallucination for multi-robot navigation in narrow hallways,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 10 033–10 039.
  34. D. M. Nguyen, M. Nazeri, A. Payandeh, A. Datar, and X. Xiao, “Toward human-like social robot navigation: A large-scale, multi-modal, social human navigation dataset,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2023.
  35. Q.-H. Vu, B.-S. Kim, and J.-B. Song, “Autonomous stair climbing algorithm for a small four-tracked robot,” in 2008 International Conference on Control, Automation and Systems.   IEEE, 2008, pp. 2356–2360.
  36. P. Fankhauser, M. Bloesch, and M. Hutter, “Probabilistic terrain mapping for mobile robots with uncertain localization,” IEEE Robotics and Automation Letters (RA-L), vol. 3, no. 4, pp. 3019–3026, 2018.
  37. A. Kumar, Z. Fu, D. Pathak, and J. Malik, “Rma: Rapid motor adaptation for legged robots,” in Robotics: Science and Systems, 2021.
  38. C. Zheng, S. Sane, K. Lee, V. Kalyanram, and K. Lee, “α𝛼\alphaitalic_α-waltr: Adaptive wheel-and-leg transformable robot for versatile multiterrain locomotion,” IEEE Transactions on Robotics, 2022.
  39. K. Xu, Y. Lu, L. Shi, J. Li, S. Wang, and T. Lei, “Whole-body stability control with high contact redundancy for wheel-legged hexapod robot driving over rough terrain,” Mechanism and Machine Theory, vol. 181, p. 105199, 2023.
  40. C. Wright, A. Johnson, A. Peck, Z. McCord, A. Naaktgeboren, P. Gianfortoni, M. Gonzalez-Rivero, R. Hatton, and H. Choset, “Design of a modular snake robot,” in 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2007, pp. 2609–2614.
  41. D. Rollinson and H. Choset, “Virtual chassis for snake robots,” in 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2011, pp. 221–226.
  42. X. Xiao, B. Liu, G. Warnell, and P. Stone, “Motion planning and control for mobile robot navigation using machine learning: a survey,” Autonomous Robots, vol. 46, no. 5, pp. 569–597, 2022.
  43. M. Sivaprakasam, S. Triest, W. Wang, P. Yin, and S. Scherer, “Improving off-road planning techniques with learned costs from physical interactions,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 4844–4850.
  44. H. Karnan, K. S. Sikand, P. Atreya, S. Rabiee, X. Xiao, G. Warnell, P. Stone, and J. Biswas, “Vi-ikd: High-speed accurate off-road navigation using learned visual-inertial inverse kinodynamics,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2022, pp. 3294–3301.
  45. D. Vasquez, B. Okal, and K. O. Arras, “Inverse reinforcement learning algorithms and features for robot navigation in crowds: an experimental comparison,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2014, pp. 1341–1346.
  46. M. Wigness, J. G. Rogers, and L. E. Navarro-Serment, “Robot navigation from human demonstration: Learning control behaviors,” in 2018 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2018, pp. 1150–1157.
  47. K. S. Sikand, S. Rabiee, A. Uccello, X. Xiao, G. Warnell, and J. Biswas, “Visual representation learning for preference-aware path planning,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 11 303–11 309.
  48. X. Xiao, B. Liu, G. Warnell, J. Fink, and P. Stone, “Appld: Adaptive planner parameter learning from demonstration,” IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4541–4547, 2020.
  49. Z. Wang, X. Xiao, B. Liu, G. Warnell, and P. Stone, “Appli: Adaptive planner parameter learning from interventions,” in 2021 IEEE international conference on robotics and automation (ICRA).   IEEE, 2021, pp. 6079–6085.
  50. Z. Wang, X. Xiao, G. Warnell, and P. Stone, “Apple: Adaptive planner parameter learning from evaluative feedback,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 7744–7749, 2021.
  51. Z. Xu, G. Dhamankar, A. Nair, X. Xiao, G. Warnell, B. Liu, Z. Wang, and P. Stone, “Applr: Adaptive planner parameter learning from reinforcement,” in 2021 IEEE international conference on robotics and automation (ICRA).   IEEE, 2021, pp. 6086–6092.
  52. X. Xiao, Z. Wang, Z. Xu, B. Liu, G. Warnell, G. Dhamankar, A. Nair, and P. Stone, “Appl: Adaptive planner parameter learning,” Robotics and Autonomous Systems, vol. 154, p. 104132, 2022.
  53. Z. Xu, X. Xiao, G. Warnell, A. Nair, and P. Stone, “Machine learning methods for local motion planning: A study of end-to-end vs. parameter learning,” in 2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR).   IEEE, 2021, pp. 217–222.
  54. M. Everett, J. Miller, and J. P. How, “Planning beyond the sensing horizon using a learned context,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2019, pp. 1064–1071.
  55. X. Meng, N. Hatch, A. Lambert, A. Li, N. Wagener, M. Schmittle, J. Lee, W. Yuan, Z. Chen, S. Deng et al., “Terrainnet: Visual modeling of complex terrain for high-speed, off-road navigation,” arXiv preprint arXiv:2303.15771, 2023.
  56. Y. Pan, C.-A. Cheng, K. Saigol, K. Lee, X. Yan, E. A. Theodorou, and B. Boots, “Imitation learning for agile autonomous driving,” The International Journal of Robotics Research, vol. 39, no. 2-3, pp. 286–302, 2020.
  57. A. Faust, K. Oslund, O. Ramirez, A. Francis, L. Tapia, M. Fiser, and J. Davidson, “Prm-rl: Long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2018, pp. 5113–5120.
  58. M. Pfeiffer, M. Schaeuble, J. Nieto, R. Siegwart, and C. Cadena, “From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots,” in IEEE International Conference on Robotics and Automation.   IEEE, 2017.
  59. X. Xiao, B. Liu, G. Warnell, and P. Stone, “Toward agile maneuvers in highly constrained spaces: Learning from hallucination,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1503–1510, 2021.
  60. X. Xiao, B. Liu, and P. Stone, “Agile robot navigation through hallucinated learning and sober deployment,” in 2021 IEEE international conference on robotics and automation (ICRA).   IEEE, 2021, pp. 7316–7322.
  61. Z. Wang, X. Xiao, A. J. Nettekoven, K. Umasankar, A. Singh, S. Bommakanti, U. Topcu, and P. Stone, “From agile ground to aerial navigation: Learning from learned hallucination,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2021, pp. 148–153.
  62. B. Liu, X. Xiao, and P. Stone, “A lifelong learning approach to mobile robot navigation,” IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 1090–1096, 2021.
  63. Z. Xu, B. Liu, X. Xiao, A. Nair, and P. Stone, “Benchmarking reinforcement learning techniques for autonomous navigation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2023, pp. 9224–9230.
  64. H. Karnan, G. Warnell, X. Xiao, and P. Stone, “Voila: Visual-observation-only imitation learning for autonomous navigation,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 2497–2503.
  65. M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang et al., “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316, 2016.
  66. S. Karaman and E. Frazzoli, “Incremental sampling-based algorithms for optimal motion planning,” Robotics Science and Systems VI, vol. 104, no. 2, 2010.
  67. S. Karaman, M. R. Walter, A. Perez, E. Frazzoli, and S. Teller, “Anytime motion planning using the rrt,” in 2011 IEEE international conference on robotics and automation.   IEEE, 2011, pp. 1478–1483.
  68. ROS, “rtabmap_ros - ros wiki,” http://wiki.ros.org/rtabmap_ros, 2023, accessed: 2023-06-07.
  69. T. Miki, L. Wellhausen, R. Grandia, F. Jenelten, T. Homberger, and M. Hutter, “Elevation mapping for locomotion and navigation using gpu,” 2022.
  70. L. Wellhausen and M. Hutter, “Artplanner: Robust legged robot navigation in the field,” in Field Robotics, 2023.
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