Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
A survey of motion planning and related geometric algorithms
Artificial Intelligence - Special issue on geometric reasoning
An algorithmic approach to some problems in terrain navigation
Artificial Intelligence - Special issue on geometric reasoning
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Robot Motion Planning
Genetic Algorithms and Robotics
Genetic Algorithms and Robotics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms and Simulated Annealing
Genetic Algorithms and Simulated Annealing
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Retraction: A new approach to motion-planning
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
Journal of Intelligent and Robotic Systems
Biologically inspired neural network approaches to real-time collision-free robot motion planning
Biologically inspired robot behavior engineering
Real-time tour construction for a mobile robot in a dynamic environment
Robotics and Autonomous Systems
Journal of Intelligent and Robotic Systems
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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A genetic algorithm for the path planning problem of a mobile robot which is moving and picking up loads on its way is presented. Assuming a findpath problem in a graph, the proposed algorithm determines a near-optimal path solution using a bit-string encoding of selected graph vertices. Several simulation results of specific task-oriented variants of the basic path planning problem using the proposed genetic algorithm are provided. The results obtained are compared with ones yielded by hill-climbing and simulated annealing techniques, showing a higher or at least equally well performance for the genetic algorithm.