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 cost on different edges of the graph
are not independent of each other. In this paper, we generalize the Probabilistic RoadMap (PRM) framework to 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