Robust online belief space planning in changing environments: Application to physical mobile robots

Agha-mohammadi, Ali-akbar; Agarwal, Saurav; Mahadevan, Aditya; Chakravorty, Suman; Tomkins, Daniel; Denny, Jory; Amato, Nancy M.

Robust online belief space planning in changing environments: Application to physical mobile robots

Type

Conference Paper

Authors

Agha-mohammadi, Ali-akbar
Agarwal, Saurav
Mahadevan, Aditya
Chakravorty, Suman
Tomkins, Daniel
Denny, Jory
Amato, Nancy M.

KAUST Grant Number

KUS-C1-016-04

Date

2014-05

Abstract

© 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.

Citation

Agha-mohammadi A, Agarwal S, Mahadevan A, Chakravorty S, Tomkins D, et al. (2014) Robust online belief space planning in changing environments: Application to physical mobile robots. 2014 IEEE International Conference on Robotics and Automation (ICRA). Available: http://dx.doi.org/10.1109/ICRA.2014.6906602.

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.