Abstract
Automatic motion planning has applications ranging from traditional robotics to computer-aided design to computational biology and chemistry. While randomized planners, such as probabilistic roadmap methods (prms) or rapidly-exploring random trees (rrt), have been highly successful in solving many high degree of freedom problems, there are still many scenarios in which we need better methods, e.g., problems involving narrow passages or which contain multiple regions that are best suited to different planners.
In this work, we present resampl, a motion planning strategy that uses local region information to make intelligent decisions about how and where to sample, which samples to connect together, and to find paths through the environment. Briefly, resampl classifies regions based on the entropy of the samples in it, and then uses these classifications to further refine the sampling. Regions are placed in a region graph that encodes relationships between regions, e.g., edges correspond to overlapping regions. The strategy for connecting samples is guided by the region graph, and can be exploited in both multi-query and single-query scenarios. Our experimental results comparing resampl to previous multi-query and single-query methods show that resampl is generally significantly faster and also usually requires fewer samples to solve the problem.
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References
Amato, N.M., Bayazit, O.B., Dale, L.K., Jones, C.V., Vallejo, D.: OBPRM: An obstacle-based PRM for 3D workspaces. In: Robotics: The Algorithmic Perspective. Proc. Third Workshop on Algorithmic Foundations of Robotics (WAFR), Houston, TX, Natick, MA, pp. 155–168. A.K. Peters (1998)
Amato, N.M., Bayazit, O.B., Dale, L.K., Jones, C.V., Vallejo, D.: Choosing good distance metrics and local planners for probabilistic roadmap methods. IEEE Trans. Robot. Automat. 16(4), 442–447 (2000)
Bohlin, R., Kavraki, L.E.: Path planning using Lazy PRM. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 521–528 (2000)
Boor, V., Overmars, M.H., van der Stappen, A.F.: The Gaussian sampling strategy for probabilistic roadmap planners. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), vol. 2, pp. 1018–1023 (1999)
Burns, B., Brock, O.: Sampling-based motion planning using predictive models. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA) (2005)
Burns, B., Brock, O.: Toward optimal configuration space sampling. In: Proc. Robotics: Sci. Sys. (RSS) (2005)
Foskey, M., Garber, M., Lin, M., Manocha, D.: A voronoi-based hybrid motion planner. In: Proc. IEEE/RSJ International Conf. on Intelligent Robots and Systems (IROS 2001) (2001)
Gottschalk, S., Lin, M.C., Manocha, D.: OBB-tree: A hierarchical structure for rapid interference detection. Comput. Graph. 30, 171–180 (1996); Proc. SIGGRAPH 1996
Hsu, D., Jiang, T., Reif, J., Sun, Z.: Bridge test for sampling narrow passages with proabilistic roadmap planners. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 4420–4426 (2003)
Hsu, D., Sánchez-Ante, G., Sun, Z.: Hybrid PRM sampling with a cost-sensitive adaptive strategy. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 3885–3891 (2005)
Kavraki, L.E., Svestka, P., Latombe, J.C., Overmars, M.H.: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Automat. 12(4), 566–580 (1996)
Kuffner, J.J., LaValle, S.M.: RRT-Connect: An Efficient Approach to Single-Query Path Planning. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 995–1001 (2000)
La Valle, S.M., Kuffner, J.J.: Rapidly-Exploring Random Trees: Progress and Prospects. In: Proc. Int. Workshop on Algorithmic Foundations of Robotics (WAFR), pp. SA45–SA59 (2000)
Morales, M., Tapia, L., Pearce, R., Rodriguez, S., Amato, N.M.: A machine learning approach for feature-sensitive motion planning. In: Proc. Int. Workshop on Algorithmic Foundations of Robotics (WAFR), Utrecht/Zeist, The Netherlands, pp. 316–376 (July 2004)
Nielsen, C.L., Kavraki, L.E.: A two level fuzzy PRM for manipulation planning. Technical Report TR2000-365, Computer Science, Rice University, Houston, TX (2000)
Redon, S., Lin, M.C.: Practical local planning in the contact space. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA) (April 2005)
Reif, J.H.: Complexity of the mover’s problem and generalizations. In: Proc. IEEE Symp. Foundations of Computer Science (FOCS), San Juan, Puerto Rico, pp. 421–427 (October 1979)
Rodriguez, S., Thomas, S., Pearce, R., Amato, N.M.: Resampl: A region-sensitive adaptive motion planner. Technical Report TR06-004, Parasol Lab, Dept. of Computer Science, Texas A&M University (March 2006)
Song, G., Miller, S.L., Amato, N.M.: Customizing PRM roadmaps at query time. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 1500–1505 (2001)
Varadhan, G., Manocha, D.: Star-shaped roadmaps: A deterministic sampling approach for complete motion planning. In: Proc. Robotics: Sci. Sys. (RSS) (2005)
Wilmarth, S.A., Amato, N.M., Stiller, P.F.: MAPRM: A probabilistic roadmap planner with sampling on the medial axis of the free space. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), vol. 2, pp. 1024–1031 (1999)
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Rodriguez, S., Thomas, S., Pearce, R., Amato, N.M. (2008). RESAMPL: A Region-Sensitive Adaptive Motion Planner. In: Akella, S., Amato, N.M., Huang, W.H., Mishra, B. (eds) Algorithmic Foundation of Robotics VII. Springer Tracts in Advanced Robotics, vol 47. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68405-3_18
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DOI: https://doi.org/10.1007/978-3-540-68405-3_18
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