Genetic Algorithms for Adaptive Motion Planning of an Autonomous Mobile Robot
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
An Improved Genetic Algorithm of Optimum Path Planning for Mobile Robots
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
FPGA Implementation of Genetic Algorithm for UAV Real-Time Path Planning
Journal of Intelligent and Robotic Systems
Efficient and safe path planning for a mobile robot using genetic algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A Static Environment-Based Path Planning Method by Using Genetic Algorithm
CCIE '10 Proceedings of the 2010 International Conference on Computing, Control and Industrial Engineering - Volume 02
Path planning strategy for autonomous mobile robot navigation using Petri-GA optimisation
Computers and Electrical Engineering
An efficient dynamic system for real-time robot-path planning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic Algorithms for Route Discovery
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mobile robot path planning using polyclonal-based artificial immune network
Journal of Control Science and Engineering - Special issue on Advances in Methods for Networked and Cyber-Physical System
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In this study, a new mutation operator is proposed for the genetic algorithm (GA) and applied to the path planning problem of mobile robots in dynamic environments. Path planning for a mobile robot finds a feasible path from a starting node to a target node in an environment with obstacles. GA has been widely used to generate an optimal path by taking advantage of its strong optimization ability. While conventional random mutation operator in simple GA or some other improved mutation operators can cause infeasible paths, the proposed mutation operator does not and avoids premature convergence. In order to demonstrate the success of the proposed method, it is applied to two different dynamic environments and compared with previous improved GA studies in the literature. A GA with the proposed mutation operator finds the optimal path far too many times and converges more rapidly than the other methods do.