TY - GEN
T1 - Sampling-based nonholonomic motion planning in belief space via Dynamic Feedback Linearization-based FIRM
AU - Agha-Mohammadi, Ali Akbar
AU - Chakravorty, Suman
AU - Amato, Nancy M.
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - In roadmap-based methods, such as the Probabilistic Roadmap Method (PRM) in deterministic environments or the Feedback-based Information RoadMap (FIRM) in partially observable probabilistic environments, a stabilizing controller is needed to guarantee node reachability in state or belief space. In belief space, it has been shown that belief-node reachability can be achieved using stationary Linear Quadratic Gaussian (LQG) controllers, for linearly controllable systems. However, for nonholonomic systems such as a unicycle model, belief reachability is a challenge. In this paper, we construct a roadmap in information space, where the local planners in partially-observable space are constructed by utilizing a Kalman filter as an estimator along with a Dynamic Feedback Linearization-based (DFL-based) controller as the belief controller. As a consequence, the task of belief stabilization to pre-defined nodes in belief space is accomplished even for nonholonomic systems. Therefore, a query-independent roadmap is generated in belief space that preserves the 'principle of optimality', required in dynamic programming solvers. This method serves as an offline POMDP solver for motion planning in belief space, which can seamlessly take obstacles into account. Experimental results show the efficiency of both individual local planners and the overall planner over the information graph for a nonholonomic model.
AB - In roadmap-based methods, such as the Probabilistic Roadmap Method (PRM) in deterministic environments or the Feedback-based Information RoadMap (FIRM) in partially observable probabilistic environments, a stabilizing controller is needed to guarantee node reachability in state or belief space. In belief space, it has been shown that belief-node reachability can be achieved using stationary Linear Quadratic Gaussian (LQG) controllers, for linearly controllable systems. However, for nonholonomic systems such as a unicycle model, belief reachability is a challenge. In this paper, we construct a roadmap in information space, where the local planners in partially-observable space are constructed by utilizing a Kalman filter as an estimator along with a Dynamic Feedback Linearization-based (DFL-based) controller as the belief controller. As a consequence, the task of belief stabilization to pre-defined nodes in belief space is accomplished even for nonholonomic systems. Therefore, a query-independent roadmap is generated in belief space that preserves the 'principle of optimality', required in dynamic programming solvers. This method serves as an offline POMDP solver for motion planning in belief space, which can seamlessly take obstacles into account. Experimental results show the efficiency of both individual local planners and the overall planner over the information graph for a nonholonomic model.
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U2 - 10.1109/IROS.2012.6385970
DO - 10.1109/IROS.2012.6385970
M3 - Conference contribution
AN - SCOPUS:84872293325
SN - 9781467317375
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 4433
EP - 4440
BT - 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2012
T2 - 25th IEEE/RSJ International Conference on Robotics and Intelligent Systems, IROS 2012
Y2 - 7 October 2012 through 12 October 2012
ER -