Evolutionary Techniques for Mobile Robot Navigation

2012 ◽  
Vol 433-440 ◽  
pp. 6646-6651
Author(s):  
Soh Chin Yun ◽  
S. Parasuraman ◽  
Velappa Ganapathy
Keyword(s):  
Genetic Algorithm ◽  
Mobile Robot ◽  
Robot Navigation ◽  
Autonomous Systems ◽  
Dynamic Environment ◽  
Mobile Robot Navigation ◽  
Research Trend ◽  
Matlab Simulation ◽  
Starting Position ◽  
To Come

Current research trend in mobile robot is to build intelligent and autonomous systems that enables mobile robot to plan its motion in static and dynamic environment. In this paper, Genetic Algorithm (GA) is utilized to come out with an algorithm that enables the mobile robot to move from the starting position to the desired goal without colliding with any of the obstacles in the environment. The proposed navigation technique is capable of re-planning new optimum collision free path in the event of mobile robot encountering dynamic obstacles. The method is verified using MATLAB simulation and validated by Team AmigoBotTM robot. The results obtained from MATLAB simulation and real time implementation are discussed at the end of the paper.

Genetic Q-Fuzzy Based Intelligent Control for Mobile Robot Navigation

2004 ◽  
Author(s):  
V. Ram Mohan Parimi ◽  
Devendra P. Garg
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Mobile Robot ◽  
Fuzzy Logic Controller ◽  
Learning Algorithm ◽  
Robot Navigation ◽  
Path Tracking ◽  
Mobile Robot Navigation ◽  
Reinforcement Learning Algorithm

This paper deals with the design and optimization of a Fuzzy Logic Controller that is used in the obstacle avoidance and path tracking problems of mobile robot navigation. The Fuzzy Logic controller is tuned using reinforcement learning controlled Genetic Algorithm. The operator probabilities of the Genetic Algorithm are adapted using reinforcement learning technique. The reinforcement learning algorithm used in this paper is Q-learning, a recently developed reinforcement learning algorithm. The performance of the Fuzzy-Logic Controller tuned with reinforcement controlled Genetic Algorithm is then compared with the one tuned with uncontrolled Genetic Algorithm. The theory is applied to a two-wheeled mobile robot’s path tracking problem. It is shown that the performance of the Fuzzy-Logic controller tuned by Genetic Algorithm controlled via reinforcement learning is better than the performance of the Fuzzy-Logic controller tuned via uncontrolled Genetic Algorithm.

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