Motion planning under uncertainty for robotic tasks with long time horizons

International Journal of Robotics Research

Volumn 30, Issue 3, 2011, Pages 308-323

  Kurniawati, Hanna ^a   Du, Yanzhu ^b   Hsu, David ^c   Lee, Wee Sun ^c  

^a SINGAPORE MIT ALLIANCE   (Singapore)

^b Stanford University   (United States)

^c NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

Motion planning under uncertainty; POMDP; robot motion planning

Indexed keywords

3-D ENVIRONMENTS; AUTONOMOUS ROBOT; BELIEF SPACE; DYNAMIC ENVIRONMENTS; EFFECTIVE PLANNING; PARTIALLY OBSERVABLE MARKOV DECISION PROCESS; PLANNING UNDER UNCERTAINTY; POINT-BASED; POMDP; PROBABILISTIC SAMPLING; ROBOT MOTION PLANNING; ROBOTIC TASKS; STATE INFORMATION; STATE SPACE; STATE SPACE INFORMATION; TIME HORIZONS;

MARKOV PROCESSES; MOTION PLANNING; ROBOTICS; ROBOTS; THREE DIMENSIONAL;

ROBOT PROGRAMMING;

EID: 79952126758     PISSN: 02783649     EISSN: 17413176     Source Type: Journal    
DOI: 10.1177/0278364910386986     Document Type: Conference Paper

References (25)

1. Alterovitz R., Simeon T. and Goldberg K. (2007) The stochastic motion roadmap: A sampling framework for planning with Markov motion uncertainty. Proceedings Robotics: Science and Systems.
2. Choset H., Lynch KM, Hutchinson S., Kantor G., Burgard W., Kavraki LE and Thrun S. (2005) Principles of Robot Motion: Theory, Algorithms, and Implementations. The MIT Press.
3. Erwig M. (2000) The graph Voronoi diagram with applications. Networks 36: 156-163.
4. Fallon MF, Papadopoulos G. and Leonard JJ (2009) Cooperative AUV navigation using a single surface craft. Proceedings of Field and Service Robotics.
5. Hsiao K., Kaelbling LP and Lozano-Perez T. (2007) Grasping POMDPs. Proceedings of the IEEE International Conference on Robotics and Automation, pp. 4685-4692.
6. Hsu D., Latombe JC and Kurniawati H. (2006) On the probabilistic foundations of probabilistic roadmap planning. International Journal of Robotics Research 25: 627-643. (Pubitemid 43936805)
7. Hsu D., Lee WS and Rong N. (2007) What makes some POMDP problems easy to approximate? Advances in Neural Information Processing Systems (NIPS).
8. Hsu D., Lee WS and Rong N. (2008) A point-based POMDP planner for target tracking. Proceedings IEEE International Conference on Robotics and Automation, pp. 2644-2650.
9. Kaelbling L., Littman M. and Cassandra A. (1998) Planning and acting in partially observable stochastic domains. Artificial Intelligence 101: 99-134. (Pubitemid 128387390)
10. Kavraki LE, Švestka P., Latombe J-C. and Overmars MH (1996) Probabilistic roadmaps for path planning in high-dimensional configuration space. IEEE Transactions on Robotics and Automation 12: 566-580. (Pubitemid 126774162)
11. Kurniawati H., Hsu D. and Lee WS (2008) SARSOP: Efficient point-based POMDP planning by approximating optimally reachable belief spaces. Proceedings of the Robotics: Science and Systems.
12. Latombe J-C. (1991) Robot Motion Planning. Kluwer.
13. Ooi BH, Zheng H., Kurniawati H., Cho W., Dao MH, Wei J., et al. (2009) Multi-vehicle oceanographic feature exploration. Proceedings of the International Offshore and Polar Engineering Conference.
14. Pineau J., Gordon G. and Thrun S. (2003 a) Point-based value iteration: An anytime algorithm for POMDPs. International Conference on Artificial Intelligence, pp. 1025-1032.
15. Pineau J., Gordon G. and Thrun S. (2003 b) Policy-contingent abstraction for robust robot control. Proceedings of Uncertainty in Artificial Intelligence, pp. 477-484.
16. Pineau J., Montemerlo M., Pollack M., Roy N. and Thrun S. (2003) Towards robotic assistants in nursing homes: Challenges and results. Robotics and Autonomous Systems 42: 271-281.
17. Prentice S. and Roy N. (2007) The belief roadmap: Efficient planning in linear POMDPs by factoring the covariance. Proceedings of the International Symposium on Robotics Research.
18. Roy N., Gordon G. and Thrun S. (2005) Finding approximate POMDP solutions through belief compression. Journal of Artificial Intelligence Research 23: 1-40. (Pubitemid 41525865)
19. Russell S. and Norvig P. (2002) Artificial Intelligence: A Modern Approach. Prentice Hall.
20. Shani G., Brafman RI and Shimony SE (2007) Forward search value iteration for POMDPs. International Conference on Artificial Intelligence, pp. 2619-2624.
21. Smallwood RD and Sondik EJ (1973) The optimal control of partially observable Markov processes over a finite horizon. Operations Research 21: 1071-1088.
22. Smith T. and Simmons R. (2005) Point-based POMDP algorithms: Improved analysis and implementation. Proceedings Uncertainty in Artificial Intelligence.
23. Spaan MTJ and Vlassis N. (2005) Perseus: Randomized point-based value iteration for POMDPs. Journal of Artificial Intelligence Research 24: 195-220. (Pubitemid 43130936)
24. Theocharous G. and Kaelbling LP (2003) Approximate planning in POMDPs with macro-actions. Advances in Neural Information Processing Systems (NIPS).
25. Thrun S., Burgard W. and Fox D. (2005) Probabilistic Robotics. The MIT Press.