Toggle PRM: Simultaneous mapping of C-free and C-obstacle - A study in 2D

IEEE International Conference on Intelligent Robots and Systems

Volumn , Issue , 2011, Pages 2632-2639

  Denny, Jory ^a   Amato, Nancy M ^a  

^a Department of Electrical and Computer Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

C-SPACE; OVERALL EFFICIENCY; SAMPLING DENSITIES; SAMPLING-BASED;

INTELLIGENT ROBOTS; ROBOTICS;

ROBOT PROGRAMMING;

EID: 84455202774     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2011.6048865     Document Type: Conference Paper

References (24)

1. N. M. Amato, O. B. Bayazit, L. K. Dale, C. V. Jones, and D. Vallejo. OBPRM: An obstacle-based PRM for 3D workspaces. In Robotics: The Algorithmic Perspective, pages 155-168, Natick, MA, 1998. A.K. Peters. Proc. Third Workshop on Algorithmic Foundations of Robotics (WAFR), Houston, TX, 1998.
2. R. Bohlin and L. E. Kavraki. Path planning using Lazy PRM. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 521-528, 2000.
3. V. Boor, M. H. Overmars, and A. F. van der Stappen. The Gaussian sampling strategy for probabilistic roadmap planners. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), volume 2, pages 1018-1023, May 1999.
4. B. Burns and O. Brock. Sampling-based motion planning using predictive models. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 3313-3318, 2005.
5. B. Burns and O. Brock. Toward optimal configuration space sampling. In Proc. Robotics: Sci. Sys. (RSS), pages 105-112, 2005.
6. S. Gottschalk, M. C. Lin, and D. Manocha. OBB-tree: A hierarchical structure for rapid interference detection. Comput. Graph., 30:171-180, 1996. Proc. SIGGRAPH '96.
7. D. Hsu, T. Jiang, J. Reif, and Z. Sun. Bridge test for sampling narrow passages with probabilistic roadmap planners. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 4420-4426, 2003.
8. D. Hsu, L. E. Kavraki, J.-C. Latombe, R. Motwani, and S. Sorkin. On finding narrow passages with probabilistic roadmap planners. In Proc. Int. Workshop on Algorithmic Foundations of Robotics (WAFR), pages 141-153, 1998.
9. D. Hsu, G. Sánchez-Ante, and Z. Sun. Hybrid PRM sampling with a cost-sensitive adaptive strategy. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 3885-3891, 2005.
10. L. E. Kavraki, P. Švestka, J. C. Latombe, and M. H. Overmars. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Automat., 12(4):566-580, August 1996. (Pubitemid 126774162)
11. J. J. Kuffner and S. M. LaValle. RRT-Connect: An Efficient Approach to Single-Query Path Planning. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 995-1001, 2000.
12. S. M. LaValle and J. J. Kuffner. Randomized kinodynamic planning. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 473-479, 1999.
13. J.-M. Lien, O. B. Bayazit, R.-T. Sowell, S. Rodriguez, and N. M. Amato. Shepherding behaviors. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 4159-4164, April 2004.
14. J.-M. Lien, S. L. Thomas, and N. M. Amato. A general framework for sampling on the medial axis of the free space. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 4439-4444, September 2003.
15. T. Lozano-Pérez and M. A. Wesley. An algorithm for planning collision-free paths among polyhedral obstacles. Communications of the ACM, 22(10):560-570, October 1979. (Pubitemid 10424755)
16. M. Morales, L. Tapia, R. Pearce, S. Rodriguez, and N. M. Amato. A machine learning approach for feature-sensitive motion planning. In Algorithmic Foundations of Robotics VI, pages 361-376. Springer, Berlin/Heidelberg, 2005. book contains the proceedings of the International Workshop on the Algorithmic Foundations of Robotics (WAFR), Utrecht/Zeist, The Netherlands, 2004.
17. M. A. Morales A., R. Pearce, and N. M. Amato. Metrics for analyzing the evolution of C-Space models. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 1268-1273, May 2006.
18. M. A. Morales A., L. Tapia, R. Pearce, S. Rodriguez, and N. M. Amato. C-space subdivision and integration in feature-sensitive motion planning. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pages 3114-3119, April 2005.
19. C. L. Nielsen and L. E. Kavraki. A two level fuzzy PRM for manipulation planning. Technical Report TR2000-365, Computer Science, Rice University, Houston, TX, 2000.
20. J. H. Reif. Complexity of the mover's problem and generalizations. In Proc. IEEE Symp. Foundations of Computer Science (FOCS), pages 421-427, San Juan, Puerto Rico, October 1979.
21. S. Rodriguez, S. Thomas, R. Pearce, and N. M. Amato. (RESAMPL): A region-sensitive adaptive motion planner. In Algorithmic Foundation of Robotics VII, pages 285-300. Springer, Berlin/Heidelberg, 2008. book contains the proceedings of the International Workshop on the Algorithmic Foundations of Robotics (WAFR), New York City, 2006.
22. A. P. Singh, J.-C. Latombe, and D. L. Brutlag. A motion planning approach to flexible ligand binding. In Int. Conf. on Intelligent Systems for Molecular Biology (ISMB), pages 252-261, 1999.
23. S. A. Wilmarth, N. M. Amato, and P. F. Stiller. MAPRM: A probabilistic roadmap planner with sampling on the medial axis of the free space. In Proc. IEEE Int. Conf. Robot. Autom. (ICRA), volume 2, pages 1024-1031, 1999.
24. S. W. H. Wong and M. Jenkin. Exploiting collision information in probabilistic roadmap planning. In Int. Conf. on Mechatronics, 2009.