DocumentCode :
3146495
Title :
An evolutionary method for active learning of mobile robot path planning
Author :
Zhang, Byoung-Tak ; Kim, Sung-Hoon
Author_Institution :
Dept. of Comput. Eng., Seoul Nat. Univ., South Korea
fYear :
1997
fDate :
10-11 Jul 1997
Firstpage :
312
Lastpage :
317
Abstract :
Several evolutionary algorithms have been proposed for robot path planning. Most existing methods for evolutionary path planning require a number of generations for finding a satisfactory trajectory and thus are not efficient enough for real-time applications. In this paper we present a new method for evolutionary path planning which can be used online in real-time. We use an evolutionary algorithm as a means for active learning of a route map for the path planner. Given a source-destination pair, the path planner searches the map for a best matching route. If an acceptable match is not found, the planner uses another evolutionary algorithm to generate online a path for the source-destination pair. The overall system is an incremental learning planner that gradually expands its own knowledge suitable for path planning in real-time. Simulations have been performed in the domain of robotic soccer to demonstrate the effectiveness of the presented method
Keywords :
encoding; genetic algorithms; learning (artificial intelligence); mobile robots; path planning; real-time systems; search problems; active learning; encoding; evolutionary algorithms; incremental learning; mobile robot; path planning; real-time system; robotic soccer; route matching; search problem; Application software; Computational modeling; Evolutionary computation; Genetic algorithms; Mobile robots; Orbital robotics; Path planning; Real time systems; Testing; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence in Robotics and Automation, 1997. CIRA'97., Proceedings., 1997 IEEE International Symposium on
Conference_Location :
Monterey, CA
Print_ISBN :
0-8186-8138-1
Type :
conf
DOI :
10.1109/CIRA.1997.613874
Filename :
613874
Link To Document :
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