Path planning for a mobile robot using genetic algorithms

Abstract

Abstract - This paper presents a new algorithm for global path planning to a goal for a mobile robot using Genetic Algorithm (GA). A genetic algorithm is used to find the optimal path for a mobile robot to move in a static 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 robot has a starting point and a target point under the assumption that the robot passes each point only once or not at all. The obtained results in simulation affirmed the potential of the proposed algorithm. I. I NTRODUCTION The path planning problem of a mobile robot can be stated as: given (starting location, goal location, 2-D map of workplace including static obstacles), plan a collision-free path between two specified points in satisfying an optimization criterion with constraints (most commonly: shortest path). The path planning problem is computationally very expensive. Although a great deal of research has been performed to further a solution to this problem, conventional approaches tend to be inflexible in response to: • Different optimization goals and changes of goals • Uncertainties in an environments and • Different constraints on computational resources. A review of the existing approaches for solving path-planning problem is provided in [1]. Many methods have been reported to generate an optimal path such: dynamic programming and distance transform methods. In the