A software tool for teaching of particle swarm optimization fundamentals
Advances in Engineering Software
An efficient path planning algorithm for mobile robot using improved potential field
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Navigation of autonomous robots using genetic algorithms
CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
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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.