FIRM: Feedback controller-based Information-state Roadmap - A framework for motion planning under uncertainty - Conference Paper uri icon


  • Direct transformation of sampling-based motion planning methods to the Information-state (belief) space is a challenge. The main bottleneck for roadmap-based techniques in belief space is that the incurred costs on different edges of the graph are not independent of each other. In this paper, we generalize the Probabilistic RoadMap (PRM) framework to obtain a Feedback controller-based Information-state RoadMap (FIRM) that takes into account motion and sensing uncertainty in planning. The FIRM nodes and edges lie in belief space and the crucial feature of FIRM is that the costs associated with different edges of FIRM are independent of each other. Therefore, this construct essentially breaks the "curse of history" in the original Partially Observable Markov Decision Process (POMDP), which models the planning problem. Further, we show how obstacles can be rigorously incorporated into planning on FIRM. All these properties stem from utilizing feedback controllers in the construction of FIRM. 2011 IEEE.

name of conference

  • 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)

published proceedings

  • 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

author list (cited authors)

  • Agha-Mohammadi, A., Chakravorty, S., & Amato, N. M.

complete list of authors

  • Agha-Mohammadi, Ali-Akbar||Chakravorty, S||Amato, NM

publication date

  • September 2011