Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Real-time obstacle avoidance for manipulators and mobile robots
Autonomous robot vehicles
New technique of mobile robot navigation using a hybrid adaptive fuzzy-potential field approach
ICC&IE Selected papers from the 22nd ICC&IE conference on Computers & industrial engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Casper: Space Exploration through Continuous Planning
IEEE Intelligent Systems
Path planning and prototype design of an AGV
Mathematical and Computer Modelling: An International Journal
A symbol-based intelligent control system with self-exploration process
Engineering Applications of Artificial Intelligence
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
Path planning strategy for autonomous mobile robot navigation using Petri-GA optimisation
Computers and Electrical Engineering
Journal of Intelligent and Robotic Systems
Multipoint potential field method for path planning of autonomous underwater vehicles in 3D space
Intelligent Service Robotics
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In this paper is presented a new scheme for autonomous navigation of a mobile robot, based on improved artificial potential fields and a genetic algorithm. In conventional artificial potential field methods, the robot is attracted by the goal position only, and rejected by several obstacles. Use of a single attraction point can lead to trap situations where the method is unable to produce the resultant force needed to avoid large obstacles. In the scheme presented here, multiple auxiliary attraction points have been used to allow the robot to avoid large, or closely spaced, obstacles. The configuration of the optimum potential field is automatically determined by a genetic algorithm. Simulation experiments performed with three different obstacle configurations, and ten different routes, showed that the scheme reported has a good performance in environments with high obstacle densities, achieving a success rate of 93 per cent.