This paper presents a new algorithm for global path planning to a goal for a mobile robot using GA and fuzzy Algorithms. A genetic algorithm is used to find the optimal path for a mobile robot to move in a dynamic environment expressed by a map with nodes and links. Locations of target and obstacles to find an optimal path are given in an environment that is a 2-D workplace. Each via point (landmark) in the net is a gene which is represented using binary code. The number of genes in one chromosome is function of the number of obstacles in the map. Therefore, we used a fixed length chromosome. The generated robot path is optimal in the sense of the shortest distance. The fitness function of genetic algorithm takes full consideration of three factors: the collision avoidance path, the shortest distance and smoothness of the path. The specific genetic operators are also selected to make the genetic algorithm more effective. The simulation results verify that the genetic algorithm is high effective under various complex dynamic alien environments.
Authors in REF have used a genetic algorithm and a fuzzy logic system for a mobile robot path planning.
A Novel Implementation of G-Fuzzy Logic Controller Algorithm on Mobile Robot Motion Planning Problem
Computer and Information Science
Computer and Information Science
Computer Science