TY - GEN
T1 - FIRM
T2 - 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics, IROS'11
AU - Agha-Mohammadi, Ali Akbar
AU - Chakravorty, Suman
AU - Amato, Nancy M.
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84455173030&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84455173030&partnerID=8YFLogxK
U2 - 10.1109/IROS.2011.6048702
DO - 10.1109/IROS.2011.6048702
M3 - Conference contribution
AN - SCOPUS:84455173030
SN - 9781612844541
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 4284
EP - 4291
BT - IROS'11 - 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Y2 - 25 September 2011 through 30 September 2011
ER -