The BDD-Based Dynamic A* Algorithm for Real-Time Replanning

Finding optimal path through a graph efficiently is central to many problems, including route planning for a mobile robot. BDD-based incremental heuristic search method uses heuristics to focus their search and reuses BDD-based information from previous searches to find solutions to series of similar search problems much faster than solving each search problem from scratch. In this paper, we apply BDD-based incremental heuristic search to robot navigation in unknown terrain, including goal-directed navigation in unknown terrain and mapping of unknown terrain. The resulting BDD-based dynamic A* (BDDD*) algorithm is capable of planning paths in unknown, partially known and changing environments in an efficient, optimal, and complete manner. We present properties about BDDD* and demonstrate experimentally the advantages of combining BDD-based incremental and heuristic search for the applications studied. We believe that our experimental results will make BDD-based D* like replanning algorithms more popular and enable robotics researchers to adapt them to additional applications.

[1]  Otthein Herzog,et al.  KI-98: Advances in Artificial Intelligence , 1998, Lecture Notes in Computer Science.

[2]  Frank Reffel,et al.  OBDDs in Heuristic Search , 1998, KI.

[3]  Manuela M. Veloso,et al.  State-set branching: Leveraging BDDs for heuristic search , 2008, Artif. Intell..

[4]  David Furcy,et al.  Lifelong Planning A , 2004, Artif. Intell..

[5]  Kaile Su,et al.  BDDRPA*: An Efficient BDD-Based Incremental Heuristic Search Algorithm for Replanning , 2006, Australian Conference on Artificial Intelligence.

[6]  Judea Pearl,et al.  Heuristics : intelligent search strategies for computer problem solving , 1984 .

[7]  Anthony Stentz,et al.  The Focussed D* Algorithm for Real-Time Replanning , 1995, IJCAI.

[8]  Anthony Stentz,et al.  DD* Lite: Efficient Incremental Search with State Dominance , 2006, AAAI.

[9]  Anthony Stentz,et al.  The Delayed D* Algorithm for Efficient Path Replanning , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[10]  Daniele Frigioni,et al.  Fully Dynamic Algorithms for Maintaining Shortest Paths Trees , 2000, J. Algorithms.

[11]  Maxim Likhachev,et al.  D*lite , 2002, AAAI/IAAI.