Mobile Robot Path Planning Using Genetic Algorithm in a Static Environment

Abstract

Genetic algorithm, developed by Goldberg, has been used to solve optimization problems, and in modeling systems where randomness is involved. This paper presents a review of the path planning optimization problem, and an algorithm for robot path planning in a static environment, using genetic algorithm as a tool. The purpose of the algorithm is to find the fastest route in a given number of steps, while avoiding obstacles in the space. The algorithm's performance is enhanced by automatically omitting all suggested routes that cross the boundaries of the environment. For optimization and search problems, genetic algorithms are used as a search tool in computing to find exact or an approximate solution. The algorithm is tested with number of steps less, equal, and more than the sufficient steps needed. The results show how the number of steps in different environments set the best path to be followed.