3Dana: Path Planning on 3D Surfaces
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An important issue when planning the tasks that a mobile robot has to reach is the path that it has to follow. In that sense, classical path planning algorithms focus on minimizing the total distance, generally assuming a flat terrain. Newer approaches also include traversability cost maps to define the terrain characteristics. However, this approach may generate unsafe paths in realistic environments as the terrain relief is lost in the discretisation. In this paper we will focus on the path planning problem when dealing with a Digital Terrain Model (DTM). Over such DTM we have developed 3Dana, an any-angle path planning algorithm. The objective is to obtain candidate paths that may be longer than the ones obtained with classical algorithms, but safer. Also, in 3Dana we can consider other parameters to maximize the path optimality: the maximum slope allowed by the robot and the heading changes during the path. These constraints allow discarding infeasible paths, while minimizing the heading changes of the robot. To demonstrate the effectiveness of the algorithm proposed, we present the results for the paths obtained for real Mars DTMs.
KeywordsPath Planning Slope DTM Rover
Pablo Muñoz is supported by the European Space Agency under the Networking and Partnering Initiative “Cooperative Systems for Autonomous Exploration Missions” project 4000106544/ 12/NL/PA. The work is supported by MINECO project EphemeCH TIN2014-56494-C4-4-P and UAH 2015/00297/001. Authors want to thanks the reviewers for their valuable comments.
- 1.Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4, 100–107 (1968)Google Scholar
- 3.Muñoz, P., R-Moreno, M.D.: S-Theta*: low steering path-planning algorithm. In: Proceedings of the 32nd SGAI International Conference, Cambridge, UK, Dec 2012, pp. 109–121Google Scholar
- 4.Yap, P.: Grid-based path-finding. In: Advances in Artificial Intelligence, Lecture Notes in Computer Science, vol. 2338. Springer, Berlin/Heidelberg (2002)Google Scholar
- 6.Akinc, M., Bekris, K.E., Chen, B.Y., Ladd, A.M., Plaku, E., Kavraki, L.E.: Probabilistic roadmaps of trees for parallel computation of multiple query roadmaps. In: Robotics Research. The Eleventh International Symposium, Springer Tracts in Advanced Robotics, vol. 15, pp. 80–89. Springer, Berlin, Heidelberg (2005)Google Scholar
- 7.Millington, I., Funge, J.: Artificial Intelligence for Games, 2nd edn. Morgan Kaufmann Publishers (2009)Google Scholar
- 9.Garcia, A., Barrientos, A., Medina, A., Colmenarejo, P., Mollinedo, L., Rossi, C.: 3D path planning using a fuzzy logic navigational map for planetary surface rovers. In: Proceedings of the 11th Symposium on Advanced Space Technologies in Robotics and Automation (2011)Google Scholar
- 10.Ishigami, G., Nagatani, K., Yoshida, K.: Path planning for planetary exploration rovers and its evaluation based on wheel slip dynamics. In: Proceedings of the IEEE International Conference on Robotics and Automation, Roma, Italy, Apr 2007, pp. 2361–2366Google Scholar
- 12.Page, D.L., Koschan, A.F., Abidi, M.A.: Ridge-valley path planning for 3D terrains. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, Orlando, Florida, USA, May 2006, pp. 119–124Google Scholar
- 13.Foley, J., van Dam, A., Feiner, S., Hughes, J.: Computer Graphics: Principles and Practice. Addison-Wesley (1992)Google Scholar
- 14.Kirk, R.L., Howington-Kraus, E., Rosiek, M.R., Anderson, J.A., Archinal, B.A., Becker, K.J., Cook, D.A., Galuszka, D., Geissler, P.E., Hare, T.M., Holmberg, I.M., Keszthelyi, L.P., Redding, B.L., Delamere, W.A., Gallagher, D., Chapel, J., Eliason, E.M., King, R., McEwen, A.S.: Ultrahigh resolution topographic mapping of Mars with MRO HiRISE stereo images: meter-scale slopes of candidate Phoenix landing sites. J. Geophys. Res.: Planets 113(E3), n/a–n/a (2008)Google Scholar