Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 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

Autonomous Mapless Navigation on Uneven Terrains (2402.13443v1)

Published 21 Feb 2024 in cs.RO, cs.SY, and eess.SY

Abstract: We propose a new method for autonomous navigation in uneven terrains by utilizing a sparse Gaussian Process (SGP) based local perception model. The SGP local perception model is trained on local ranging observation (pointcloud) to learn the terrain elevation profile and extract the feasible navigation subgoals around the robot. Subsequently, a cost function, which prioritizes the safety of the robot in terms of keeping the robot's roll and pitch angles bounded within a specified range, is used to select a safety-aware subgoal that leads the robot to its final destination. The algorithm is designed to run in real-time and is intensively evaluated in simulation and real world experiments. The results compellingly demonstrate that our proposed algorithm consistently navigates uneven terrains with high efficiency and surpasses the performance of other planners. The code and video can be found here: https://rb.gy/3ov2r8

Definition Search Book Streamline Icon: https://streamlinehq.com
References (29)
  1. M. Ali and L. Liu, “Light-weight pointcloud representation with sparse gaussian process,” pp. 4931–4937, 2023.
  2. M. Ali, H. Jardali, N. Roy, and L. Liu, “Autonomous Navigation, Mapping and Exploration with Gaussian Processes,” in Proceedings of Robotics: Science and Systems, Daegu, Republic of Korea, July 2023.
  3. M. Ali and L. Liu, “Gp-frontier for local mapless navigation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 10 047–10 053.
  4. I. S. Mohamed, M. Ali, and L. Liu, “Gp-guided mppi for efficient navigation in complex unknown cluttered environments,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023, pp. 7463–7470.
  5. B. Zhou, J. Yi, X. Zhang, L. Chen, D. Yang, F. Han, and H. Zhang, “An autonomous navigation approach for unmanned vehicle in outdoor unstructured terrain with dynamic and negative obstacles,” Robotica, vol. 40, no. 8, pp. 2831–2854, 2022.
  6. T. H. Y. Leung, D. Ignatyev, and A. Zolotas, “Hybrid terrain traversability analysis in off-road environments,” in 2022 8th International Conference on Automation, Robotics and Applications (ICARA), 2022, pp. 50–56.
  7. P. Fankhauser, M. Bloesch, and M. Hutter, “Probabilistic terrain mapping for mobile robots with uncertain localization,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3019–3026, 2018.
  8. A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, “Octomap: An efficient probabilistic 3d mapping framework based on octrees,” Autonomous robots, vol. 34, pp. 189–206, 2013.
  9. P. Kim, J. Chen, J. Kim, and Y. K. Cho, “Slam-driven intelligent autonomous mobile robot navigation for construction applications,” in Advanced Computing Strategies for Engineering: 25th EG-ICE International Workshop 2018, Lausanne, Switzerland, June 10-13, 2018, Proceedings, Part I 25.   Springer, 2018, pp. 254–269.
  10. Z. Jian, Z. Lu, X. Zhou, B. Lan, A. Xiao, X. Wang, and B. Liang, “Putn: A plane-fitting based uneven terrain navigation framework,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, pp. 7160–7166.
  11. M. Wermelinger, P. Fankhauser, R. Diethelm, P. Krüsi, R. Siegwart, and M. Hutter, “Navigation planning for legged robots in challenging terrain,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, pp. 1184–1189.
  12. A. Chilian and H. Hirschmüller, “Stereo camera based navigation of mobile robots on rough terrain,” in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009, pp. 4571–4576.
  13. J. Wang, L. Xu, H. Fu, Z. Meng, C. Xu, Y. Cao, X. Lyu, and F. Gao, “Towards efficient trajectory generation for ground robots beyond 2d environment,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023, pp. 7858–7864.
  14. P. E. Hart, N. J. Nilsson, and B. Raphael, “A formal basis for the heuristic determination of minimum cost paths,” IEEE Transactions on Systems Science and Cybernetics, vol. 4, no. 2, pp. 100–107, 1968.
  15. A. Ammar, H. Bennaceur, I. Châari, A. Koubâa, and M. Alajlan, “Relaxed dijkstra and a* with linear complexity for robot path planning problems in large-scale grid environments,” Soft Computing, vol. 20, pp. 4149–4171, 2016.
  16. S. M. LaValle and J. J. Kuffner Jr, “Randomized kinodynamic planning,” The international journal of robotics research, vol. 20, no. 5, pp. 378–400, 2001.
  17. 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.
  18. J. Borenstein, Y. Koren, et al., “The vector field histogram-fast obstacle avoidance for mobile robots,” IEEE transactions on robotics and automation, vol. 7, no. 3, pp. 278–288, 1991.
  19. O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” The international journal of robotics research, vol. 5, no. 1, pp. 90–98, 1986.
  20. Y. Peng, W. Guo, M. Liu, J. Cui, and S. Xie, “Obstacle avoidance planning based on artificial potential field optimized by point of tangency in three-dimensional space,” Journal of System Simulation, vol. 26, no. 8, pp. 1758–1762, 2014.
  21. N. Lawrence, M. Seeger, and R. Herbrich, “Fast sparse Gaussian process methods: The informative vector machine,” in Proceedings of the 16th annual conference on neural information processing systems, 2003, pp. 609–616.
  22. E. Snelson and Z. Ghahramani, “Sparse gaussian processes using pseudo-inputs,” Advances in neural information processing systems, vol. 18, p. 1257, 2006.
  23. M. K. Titsias, “Variational model selection for sparse gaussian process regression,” Report, University of Manchester, UK, 2009.
  24. M. Titsias, “Variational learning of inducing variables in sparse gaussian processes,” in Artificial intelligence and statistics.   PMLR, 2009, pp. 567–574.
  25. K. Yan and B. Ma, “Mapless navigation based on 2d lidar in complex unknown environments,” Sensors, vol. 20, no. 20, p. 5802, 2020.
  26. C. Ye and P. Webb, “A sub goal seeking approach for reactive navigation in complex unknown environments,” Robotics and Autonomous Systems, vol. 57, no. 9, pp. 877–888, 2009.
  27. A. G. d. G. Matthews, M. Van Der Wilk, T. Nickson, K. Fujii, A. Boukouvalas, P. León-Villagrá, Z. Ghahramani, and J. Hensman, “Gpflow: A gaussian process library using tensorflow.” J. Mach. Learn. Res., vol. 18, no. 40, pp. 1–6, 2017.
  28. J. Liang, K. Weerakoon, T. Guan, N. Karapetyan, and D. Manocha, “Adaptiveon: Adaptive outdoor navigation method for stable and reliable motions,” arXiv preprint arXiv:2205.03517, 2022.
  29. M. Mujahed and B. Mertsching, “The admissible gap (ag) method for reactive collision avoidance,” in 2017 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2017, pp. 1916–1921.
Citations (2)
View on Semantic Scholar

Summary

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

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

YouTube