A Hierarchical Extension of the D* Algorithm

Abstract In this paper a contribution to the practice of path planning using a new hierarchical extension of the D* algorithm is introduced. A hierarchical graph is stratified into several abstraction levels and used to model environments for path planning. The hierarchical D* algorithm uses a down-top strategy and a set of pre-calculated trajectories in order to improve performance. This allows optimality and specially lower computational time. It is experimentally proved how hierarchical search algorithms and on-line path planning algorithms based on topological abstractions can be combined successfully.

[1]  Javier González,et al.  Hierarchical graph search for mobile robot path planning , 1998, Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146).

[2]  Il Hong Suh,et al.  A hierarchical collision-free path planning algorithm for robotics , 1995, Proceedings 1995 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human Robot Interaction and Cooperative Robots.

[3]  Gianpaolo Conte,et al.  Hierarchical path planning in a multi-robot environment with a simple navigation function , 1995, IEEE Trans. Syst. Man Cybern..

[4]  Craig A. Knoblock.  Learning Abstraction Hierarchies for Problem Solving , 1990, AAAI.

[5]  Anthony Stentz,et al.  Optimal and efficient path planning for partially-known environments , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[6]  Richard E. Korf,et al.  Real-Time Heuristic Search , 1990, Artif. Intell..

[7]  Robert C. Holte,et al.  Searching With Abstractions: A Unifying Framework and New High-Performance Algorithm 1 , 1994 .

[8]  Shashi Shekhar,et al.  Dynoraii: a Real-Time Planning Algorithm , 1993, Int. J. Artif. Intell. Tools.

[9]  Jean-Claude Latombe,et al.  Robot motion planning , 1970, The Kluwer international series in engineering and computer science.

[10]  DANIEL CAGIGAS,et al.  Hierarchical Path Search with Partial Materialization of Costs for a Smart Wheelchair , 2004, J. Intell. Robotic Syst..

[11]  Kokichi Sugihara,et al.  Genetic Algorithms for Adaptive Planning of Path and Trajectory of a Mobile Robot in 2D Terrains , 1999 .

[12]  Hitoshi Kanoh,et al.  REAL-TIME ROUTE SELECTION USING GENETIC ALGORITHMS FOR CAR NAVIGATION SYSTEMS , 2000 .

[13]  Juan Antonio Fernandez Madrigal,et al.  A general world representation for mobile robot operations , 1997 .

[14]  Elke A. Rundensteiner,et al.  Hierarchical Encoded Path Views for Path Query Processing: An Optimal Model and Its Performance Evaluation , 1998, IEEE Trans. Knowl. Data Eng..

[15]  T. W. Routen,et al.  Characteristics of a genetic based approach to path planning for mobile robots , 1996 .

[16]  Javier González,et al.  Multihierarchical Graph Search , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Javier Gonzalez,et al.  Multi-Hierarchical Representation of Large-Scale Space , 2001 .

[18]  El-Ghazali Talbi,et al.  Using Genetic Algorithms for Robot Motion Planning , 1991, Geometric Reasoning for Perception and Action.

[19]  Takahiro Sasaki,et al.  The Trailblazer Search with a Hierarchical Abstract Map , 1995, IJCAI.