On the probabilistic completeness of the sampling-based feedback motion planners in belief space

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2012, Pages 3983-3990

  Agha Mohammadi, Ali Akbar ^a   Chakravorty, Suman ^a   Amato, Nancy M ^a  

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

Author keywords

[No Author keywords available]

Indexed keywords

ROBOTICS;

ABSTRACT FRAMEWORK; BELIEF SPACE; CONFIGURATION SPACE; FEED-BACK BASED; MOTION PLANNERS; ROADMAP; SAMPLING-BASED; SAMPLING-BASED METHOD;

PLANNING;

EID: 84864456953     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICRA.2012.6225062     Document Type: Conference Paper

References (21)

1. H. Choset, K. M. Lynch, S. Hutchinson, G. Kantor, W. Burgard, L. E. Kavraki, and S. Thrun, Principles of robot motion: theory, algorithms, and implementations. MIT Press, 2005.
2. S. Lavalle, Planning algorithms. Cambridge University Press, 2006.
3. S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics. MIT Press, 2005.
4. L. Kavraki, P. Švestka, J. Latombe, and M. Overmars, "Probabilistic roadmaps for path planning in high-dimensional configuration spaces," IEEE Transactions on Robotics and Automation, vol. 12, no. 4, pp. 566-580, 1996.
5. N. Amato, B. Bayazit, L. Dale, C. Jones, and D. Vallejo, "OBPRM: An Obstacle-Basaed PRM for 3D workspaces," in wafr, 1998, pp. 155-168.
6. S. Lavalle and J. Kuffner, "Randomized kinodynamic planning," International Journal of Robotics Research, vol. 20, no. 378-400, 2001.
7. A. Agha-mohammadi, S. Chakravorty, and N. Amato, "FIRM: Feedback controller-based Information-state RoadMap -a framework for motion planning under uncertainty-," in IROS, 2011.
8. S. Prentice and N. Roy, "The belief roadmap: Efficient planning in belief space by factoring the covariance," International Journal of Robotics Research, vol. 28, no. 11-12, October 2009.
9. R. Alterovitz, T. Siméon, and K. Goldberg, "The stochastic motion roadmap: A sampling framework for planning with markov motion uncertainty," in Proceedings of Robotics: Science and Systems (RSS), June 2007.
10. S. Chakravorty and S. Kumar, "Generalized sampling-based motion planners," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 41, no. 3, pp. 855-866, 2011.
11. L. Kavraki, M. Kolountzakis, and J. Latombe, "Analysis of probabilistic roadmaps for path planning," IEEE Transactions on Robotics and Automation, vol. 14, pp. 166-171, February 1998.
12. A. Ladd and L. Kavraki, "Measure theoretic analysis of probabilistic path planning," IEEE Transactions on Robotics and Automation, vol. 20, no. 2, pp. 229-242, April 2004.
13. P. Švestka and M. Overmars, "Motion planning for car-like robots using a probabilistic learning approach," International Journal of Robotics Research, vol. 16, no. 2, pp. 119-143, 1997.
14. R. Bohlin, "Robot path planning," Ph.D. dissertation, Chalmers University of Technology, Goteborg, Sweden, 2002.
15. L. Kavraki, J. Latombe, R. Motwani, and P. Raghavan, "Randomized query processing in robot motion planning," in Proc. ACM Symp. Theory of Computing, 1995, pp. 353-362.
16. D. Hsu, "Randomized single-query motion planning in expansive spaces," Ph.D. dissertation, Department of Computer Science, Stanford University, Stanford, CA, 2000.
17. D. Bertsekas, Dynamic Programming and Optimal Control: 3rd Ed. Athena Scientific, 2007.
18. J. R. Norris, Markov Chains. Cambridge Univeristy Press, 1997.
19. J. P. Keener, Principles of Applied Mathematics: Transformation and Approximation, 2nd Edition. Westview Press, 2000.
20. S. I. Resnick, A probability path. Birkhauser, 1999.
21. A. Agha-mohammadi, S. Chakravorty, and N. Amato, "Probabilistic completeness of the belief space motion planners," Technical Report: TR11-006, Parasol Lab., CSE Dept., Texas A&M University, 2011.