A framework for using the workspace medial axis in PRM planners

Probabilistic roadmap (PRM) planners have been very successful in path planning for a wide variety of problems, especially applications involving robots with many degrees of freedom. These planners randomly sample the configuration space, building up a roadmap that connects the samples. A major problem is finding valid configurations in tight areas, and many methods have been proposed to more effectively sample these regions. By constructing a skeleton-like subset of the free regions of the workspace, these heuristics can be strengthened. The skeleton provides a concise description of the workspace topology and an efficient means of finding points with maximal clearance from the obstacles. We examine the medial axis as a skeleton, including a method to compute an approximation to it. The medial axis is a two-equidistant surface in the workspace. We form a heuristic for finding difficult configurations using the medial axis, and demonstrate its effectiveness in a planner for rigid objects in a 3D workspace.

[1]  Tsai-Yen Li,et al.  Assembly maintainability study with motion planning , 1995, Proceedings of 1995 IEEE International Conference on Robotics and Automation.

[2]  J. van Leeuwen,et al.  Discrete and Computational Geometry , 2002, Lecture Notes in Computer Science.

[3]  Howie Choset,et al.  Incremental Construction of the Generalized Voronoi Diagram , the Generalized Voronoi Graph , and the Hierarchical Generalized Voronoi Graph , 1999 .

[4]  Russell H. Taylor,et al.  Interference-Free Insertion of a Solid Body Into a Cavity: An Algorithm and a Medical Application , 1996, Int. J. Robotics Res..

[5]  Lydia E. Kavraki,et al.  Planning Paths for a Flexible Surface Patch , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[6]  Mark H. Overmars,et al.  The Gaussian sampling strategy for probabilistic roadmap planners , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[7]  Lydia E. Kavraki,et al.  Randomized preprocessing of configuration for fast path planning , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[8]  Lydia E. Kavraki,et al.  On finding narrow passages with probabilistic roadmap planners , 1998 .

[9]  Nancy M. Amato,et al.  MAPRM: a probabilistic roadmap planner with sampling on the medial axis of the free space , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[10]  Lydia E. Kavraki,et al.  Deformable volumes in path planning applications , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[11]  Mark H. Overmars,et al.  A probabilistic learning approach to motion planning , 1995 .

[12]  Leonidas J. Guibas,et al.  A probabilistic roadmap planner for flexible objects with a workspace medial-axis-based sampling approach , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[13]  Lydia E. Kavraki,et al.  A probabilistic roadmap approach for systems with closed kinematic chains , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[14]  B. Donald.  Motion Planning with Six Degrees of Freedom , 1984 .

[15]  Marshall W. Bern,et al.  A new Voronoi-based surface reconstruction algorithm , 1998, SIGGRAPH.

[16]  James U. Korein,et al.  Robotics , 2018, IBM Syst. J..

[17]  El-Ghazali Talbi,et al.  Using Genetic Algorithms for Robot Motion Planning , 1991, Geometric Reasoning for Perception and Action.

[18]  Lydia E. Kavraki,et al.  Probabilistic Roadmaps for Robot Path Planning , 1998 .

[19]  Lydia E. Kavraki,et al.  Computational Approaches to Drug Design , 1999, Algorithmica.

[20]  Lydia E. Kavraki,et al.  Probabilistic roadmaps for path planning in high-dimensional configuration spaces , 1996, IEEE Trans. Robotics Autom..

[21]  Jean-Claude Latombe,et al.  Planning motions with intentions , 1994, SIGGRAPH.

[22]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[23]  Daniel Vallejo,et al.  OBPRM: an obstacle-based PRM for 3D workspaces , 1998 .

[24]  Jean-Claude Latombe,et al.  Robot motion planning , 1970, The Kluwer international series in engineering and computer science.