Abstract
Motion planning has received much attention over the past 40 years. More than 15 years have passed since the introduction of the successful sampling-based approach known as the Probabilistic RoadMap Method (PRM). PRM and its many variants have demonstrated great success for some high-dimensional problems, but they all have some level of difficulty in the presence of narrow passages. Recently, an approach called Toggle PRM has been introduced whose performance does not degrade for 2-dimensional problems with narrow passages. In Toggle PRM, a simultaneous, coordinated mapping of both C free and C obst is performed and every connection attempt augments one of the maps – either validating an edge in the current space or adding a configuration ’witnessing’ the connection failure to the other space. In this paper, we generalize Toggle PRM to d-dimensions and show that the benefits of mapping both C free and C obst continue to hold in higher dimensions. In particular, we introduce a new narrow passage characterization, α-ε-separable narrow passages, which describes the types of passages that can be successfully mapped by Toggle PRM. Intuitively, α-ε-separable narrow passages are arbitrarily narrow regions of C free that separate regions of C obst , at least locally, such as hallways in an office building. We experimentally compare Toggle PRM with other methods in a variety of scenarios with different types of narrow passages and robots with up to 16 dof.
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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, pp. 155–168. A.K. Peters, Natick (1998); Proc. Third Workshop on Algorithmic Foundations of Robotics (WAFR), Houston, TX (1998)
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), pp. 3313–3318 (2005)
Burns, B., Brock, O.: Toward optimal configuration space sampling. In: Proc. Robotics: Sci. Sys. (RSS), pp. 105–112 (2005)
Denny, J., Amato, N.M.: Toggle PRM: Simultaneous mapping of C-free and C-obstacle - a study in 2D. In: Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), San Francisco, California, USA, pp. 2632–2639 (2011)
Denny, J., Amato, N.M.: The toggle local planner for sampling-based motion planning. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), St. Paul, Minnesota, USA, pp. 1779–1786 (2012)
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
Yeh, H.-Y.(C.), Shawna Thomas, D.E., Amato, N.M.: Uobprm: A uniformly distributed obstacle-based prm. In: Proc. IEEE Int. Conf. Intel. Rob. Syst (IROS), Vilamoura, Algarve, Portugal (2012)
Hsu, D., Jiang, T., Reif, J., Sun, Z.: Bridge test for sampling narrow passages with probabilistic roadmap planners. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 4420–4426 (2003)
Hsu, D., Kavraki, L.E., Latombe, J.C., Motwani, R., Sorkin, S.: On finding narrow passages with probabilistic roadmap planners. In: Proc. Int. Workshop on Algorithmic Foundations of Robotics (WAFR), pp. 141–153 (1998)
Kavraki, L.E., Švestka, 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)
LaValle, S.M., Kuffner, J.J.: Randomized kinodynamic planning. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 473–479 (1999)
Lien, J.M., Bayazit, O.B., Sowell, R.T., Rodriguez, S., Amato, N.M.: Shepherding behaviors. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 4159–4164 (2004)
Lien, J.M., Thomas, S.L., Amato, N.M.: A general framework for sampling on the medial axis of the free space. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 4439–4444 (2003)
Lozano-Pérez, T., Wesley, M.A.: An algorithm for planning collision-free paths among polyhedral obstacles. Communications of the ACM 22(10), 560–570 (1979)
Mirtich, B.: V-clip: Fast and robust polyhedral collision detection. ACM Transaction on Graphics 17(3), 177–208 (1996)
Morales, M., Tapia, L., Pearce, R., Rodriguez, S., Amato, N.M.: A Machine Learning Approach for Feature-Sensitive Motion Planning. In: Erdmann, M., Overmars, M., Hsu, D., der Stappen, F. (eds.) Algorithmic Foundations of Robotics VI. STAR, vol. 17, pp. 361–376. Springer, Heidelberg (2005)
Morales, M., Pearce, R., Amato, N.M.: Metrics for analyzing the evolution of C-Space models. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 1268–1273 (2006)
Morales, M., Tapia, L., Pearce, R., Rodriguez, S., Amato, N.M.: C-space subdivision and integration in feature-sensitive motion planning. In: Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp. 3114–3119 (2005)
Nielsen, C.L., Kavraki, L.E.: A two level fuzzy PRM for manipulation planning. In: Proc. IEEE Int. Conf. Intel. Rob. Syst. (IROS), pp. 1716–1722 (2000)
Pearce, R., Morales, M., Amato, N.M.: Structural improvement filtering strategy for prm. In: Proc. Robotics: Sci. Sys. (RSS) (2008)
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 (1979)
Rodriguez, S., Thomas, S., Pearce, R., Amato, N.M. (RESAMPL): A Region-Sensitive Adaptive Motion Planner. In: Akella, S., Amato, N.M., Huang, W.H., Mishra, B. (eds.) Algorithmic Foundation of Robotics VII. STAR, vol. 47, pp. 285–300. Springer, Heidelberg (2008)
Singh, A.P., Latombe, J.C., Brutlag, D.L.: A motion planning approach to flexible ligand binding. In: Int. Conf. on Intelligent Systems for Molecular Biology (ISMB), pp. 252–261 (1999)
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)
Wong, S.W.H., Jenkin, M.: Exploiting collision information in probabilistic roadmap planning. In: Int. Conf. on Mechatronics (2009)
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Denny, J., Amatoo, N.M. (2013). Toggle PRM: A Coordinated Mapping of C-Free and C-Obstacle in Arbitrary Dimension. In: Frazzoli, E., Lozano-Perez, T., Roy, N., Rus, D. (eds) Algorithmic Foundations of Robotics X. Springer Tracts in Advanced Robotics, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36279-8_18
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DOI: https://doi.org/10.1007/978-3-642-36279-8_18
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