Robust Path Planning Against Pose Errors for Mobile Robots in Rough Terrain
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We propose a novel path planning method considering pose errors for off-road mobile robots based on 3D terrain map information. Mobile robots navigating on rough terrain cannot follow a planned path perfectly because of uncertainties such as pose errors. In this work, we represent such pose errors as error ellipsoids to use on collision check with obstacles in a map. The error ellipsoids are estimated based on extended Kalman filter (EKF) that integrates motion errors and global positioning systems (GPS) observation errors. Simulation and experiment results show that the proposed method enables mobile robots to generate a robust path against pose errors in a large-scale rough terrain map.
KeywordsPath planning Rough terrain Random sampling Extended Kalman Filter Error ellipsoid
This work was in part funded by ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan).
- 1.DARPA (Defense Advanced Research Projects Agency): Grand Challenge 2005 Report to Congress (2006). http://archive.darpa.mil/grandchallenge/docs/grand_challenge_2005_report_to_congress.pdf
- 3.Richter, C., Ware, J., Roy, N.: High-speed autonomous navigation of unknown environments using learned probabilities of collision. In: Proceedings of the 2014 IEEE International Conference on Robotics and Automation, pp. 6114–6121 (2014)Google Scholar
- 4.Ji, Y., Tanaka, Y., Tamura, Y., Kimura, M., Umemura, A., Kanashima, Y., Murakami, H., Yamashita, A., Asama, H.: Adaptive motion planning based on vehicle characteristics and regulations for off-road UGVs. IEEE Trans. Ind. Inform. (under review)Google Scholar
- 6.Blackmore, L., Li, H., Williams, B.: A Probabilistic approach to optimal robust path planning with obstacles. In: Proceedings of the American Control Conference, pp. 7–13 (2011)Google Scholar
- 7.Lee, S.U., Iagnemma, K.: Robust motion planning methodology for autonomous tracked vehicles in rough environment using online slip estimation. In: Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3589–3594 (2016)Google Scholar
- 8.LaValle, S.M.: Rapidly-exploring random trees: a new tool for path planning. Computer Science Department, Iowa State University, Technical Report TR 98–11, pp. 1–4 (1998)Google Scholar
- 9.Greg, W., Gary, B.: An introduction to the kalman filter. In: Proceedings of ACM SIGGRAPH, Course 8 (2001)Google Scholar
- 10.Pan, J., Chitta, S., Manocha, D.: FCL: a general purpose library for collision and proximity queries. In: Proceedings of the 2012 IEEE International Conference on Robotics and Automation, pp. 3859–3866 (2012)Google Scholar
- 11.Ladd, A.M., Kavraki, L.E.: Fast tree-based exploration of state space for robots with dynamics. In: Algorithmic Foundations of Robotics VI, pp. 297–312 (2004)Google Scholar