www.ccsenet.org/cis Computer and Information Science Vol. 4, No. 2; March 2011 ISSN 1913-8989 E-ISSN 1913-8997 102 A Novel Implementation of G-Fuzzy Logic Controller Algorithm on Mobile Robot Motion Planning Problem Seyyed Mohammad Reza Farshchi Department of Artificial Intelligence Islamic Azad University, Mashhad Branch, Iran Tel: 98-091-5308-1125 E-mail: Shiveex@Gmail.Com Seyyed Aliye Nezhad Hoseini & Fateme Mohammadi Department of Image Science & Robotics Sadjad University, Mashhad, Iran Tel: 98-091-5157-5067 E-mail: Hosseininezhad@Sadjad.ac.ir Abstract 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. Keywords: Genetic algorithm, Fuzzy logic, Obstacle avoidance, Trajectory planning 1. Introduction 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 (Acar & Choset, 2001) Many methods have been reported to generate an optimal path such: dynamic programming and distance transform methods. In the Dynamic programming method if the start point is s P , goal point is g P and sub-goal point is i P , the path generation method is how to determine a sequence of sub-goals picking out the sub-goals from their set i P (i=1,2,…,g-1). We must calculate all possible paths and select one has the smallest cost value. Very large computing power is required, especially in environments that have many sub-goals. In the distance transform method, the task of path planning is finding a path from the goal location back to the start location. The path planning procedure covers the environment with a uniform grid and the propagate distances through free space from the goal cell. The distance wave front flows around obstacles and eventually through all free space in the environment. For any starting point within the environment representing the initial position of the mobile robot, the shortest path to the goal is traced by walking downhill via the steepest descent path. However, ambiguity of optimal paths exists where there exist two or more cells (nodes) to choose with the same least