Optimal path-planning under finite memory obstacle dynamics based on probabilistic finite state automata models

Authors:
Ishanu Chattopadhyay;Asok Ray
Affiliations:
The Pennsylvania State University, University Park, PA;The Pennsylvania State University, University Park, PA
Venue:
ACC'09 Proceedings of the 2009 conference on American Control Conference
Year:
2009

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Abstract

The ν*-planning algorithm is generalized to handle finite memory obstacle dynamics. A sufficiently long observation sequence of obstacle dynamics is algorithmically compressed via Symbolic Dynamic Filtering to obtain a probabilistic finite state model which is subsequently integrated with the navigation automaton to generate an overall model reflecting both navigation constraints and obstacle dynamics. A ν*-based solution then yields a deterministic plan that maximizes the difference of the probabilities of reaching the goal and of hitting an obstacle. The approach is validated by simulated solution of dynamic mazes.