Paper Info
Title
Optimal path-planning under finite memory obstacle dynamics based on probabilistic finite state automata models
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.
Year
DOI
Venue
2009
10.1109/ACC.2009.5160369
ACC'09 Proceedings of the 2009 conference on American Control Conference
Keywords
DocType
ISSN
finite state machines,path planning,probability,finite memory obstacle dynamics,optimal path-planning,probabilistic finite state automata models,probabilistic finite state machines,symbolic dynamic filtering,v*-planning algorithm,Language Measure,Path Planning,Probabilistic Finite State Machines,Robotics,Supervisory Control
Conference
0743-1619 E-ISBN : 978-1-4244-4524-0
ISBN
Citations 
PageRank 
978-1-4244-4524-0
1
0.36
References 
Authors
7
2
Name
Order
Citations
PageRank
Ishanu Chattopadhyay1286.91
Ray, A.2832184.32