TY - GEN
T1 - Optimal path-planning under finite memory obstacle dynamics based on probabilistic finite state automata models
AU - Chattopadhyay, Ishanu
AU - Ray, Asok
PY - 2009
Y1 - 2009
N2 - The v*-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 v*-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.
AB - The v*-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 v*-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.
KW - Language measure
KW - Path planning
KW - Probabilistic finite state machines
KW - Robotics
KW - Supervisory control
UR - https://www.scopus.com/pages/publications/70449644252
UR - https://www.scopus.com/pages/publications/70449644252#tab=citedBy
U2 - 10.1109/ACC.2009.5160369
DO - 10.1109/ACC.2009.5160369
M3 - Conference contribution
AN - SCOPUS:70449644252
SN - 9781424445240
T3 - Proceedings of the American Control Conference
SP - 2403
EP - 2408
BT - 2009 American Control Conference, ACC 2009
T2 - 2009 American Control Conference, ACC 2009
Y2 - 10 June 2009 through 12 June 2009
ER -