A Novel Implementation of G-Fuzzy Logic Controller Algorithm on Mobile Robot Motion Planning Problem

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.

Multi Objective Optimization of Trajectory Planning of Non-holonomic Mobile Robot in Dynamic Environment Using Enhanced GA by Fuzzy Motion Control and A*

“…Many researchers have proposed various techniques for autonomous mobile robot navigation and obstacle avoidance to improve the robot performance. These approaches used the artificial intelligent algorithms such as fuzzy logic, neural networks and genetic algorithms [5][6][7][8][9][10][11][12][13][14][15]. During the past several years hybrid genetic-fuzzy approach has considered as one of the most powerful technique for research of intelligent system design application [12][13][14][15].…”

“…These approaches used the artificial intelligent algorithms such as fuzzy logic, neural networks and genetic algorithms [5][6][7][8][9][10][11][12][13][14][15]. During the past several years hybrid genetic-fuzzy approach has considered as one of the most powerful technique for research of intelligent system design application [12][13][14][15]. The Proposed controller in [12] was designed using fuzzy logics theory and then, a genetic algorithm was applied to optimize the scaling factors of the fuzzy logic controller for better accuracy and smoothness of the robot trajectory.…”

“…The novel genetic fuzzy algorithm has applied in [14] to generate a dynamic path tracking for mobile robot in the unknown environment. Hence, 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.…”

Development of efficient obstacle avoidance and line following mobile robot with the integration of fuzzy logic system in static and dynamic environments

“…Authors in [7] have used a genetic algorithm and a fuzzy logic system for a mobile robot path planning. The genetic algorithm was used to compute the optimal path to destination especially in a dynamic environment due to its effective adaptability to environment changes.…”