© 2014 IEEE. Motion planning in belief space (under motion and sensing uncertainty) is a challenging problem due to the computational intractability of its exact solution. The Feedback-based Information RoadMap (FIRM) framework made an important theoretical step toward enabling roadmap-based planning in belief space and provided a computationally tractable version of belief space planning. However, there are still challenges in applying belief space planners to physical systems, such as the discrepancy between computational models and real physical models. In this paper, we propose a dynamic replanning scheme in belief space to address such challenges. Moreover, we present techniques to cope with changes in the environment (e.g., changes in the obstacle map), as well as unforeseen large deviations in the robot's location (e.g., the kidnapped robot problem). We then utilize these techniques to implement the first online replanning scheme in belief space on a physical mobile robot that is robust to changes in the environment and large disturbances. This method demonstrates that belief space planning is a practical tool for robot motion planning.
|Original language||English (US)|
|Title of host publication||2014 IEEE International Conference on Robotics and Automation (ICRA)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||8|
|State||Published - May 2014|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This research supported in part by AFOSR GrantFA9550-08-1-0038 and by NSF awards CNS-0551685, CCF-0833199,CCF-0830753, IIS-0916053, IIS-0917266, EFRI-1240483, RI-1217991, byNIH NCI R25 CA090301-11, by Chevron, IBM, Intel, Oracle/Sun and byAward KUS-C1-016-04, made by King Abdullah University of Scienceand Technology (KAUST). J. Denny supported in part by an NSF GraduateResearch Fellowship.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.