Self-learning Mobile Robot Navigation in Unknown Environment Using Evolutionary Learning

Mohammed Algabri (King Saud University, Saudi Arabia)

Hassan Mathkour (King Saud University, Saudi Arabia)

Hedjar Ramdane (King Saud University, Saudi Arabia)

Mansour Alsulaiman (King Saud University, Saudi Arabia)

Khalid Al-Mutib (King Saud University, Saudi Arabia)

Abstract: An autonomous mobile robot operating in an unstructured environment must be able to learn with dynamic changes to that environment. Learning navigation and control of mobile robot in an unstructured environment is one of the most challenging problems. Fuzzy logic control is a useful tool in the field of navigation of mobile robot. In this research, we optimized a performance of fuzzy logic controller by evolutionary learning technique. Two proposed approaches have been designed and implemented: Fuzzy Logic Controller (FLC) and Genetic-Fuzzy Controller (GA-FLC). The Genetic Algorithm is used for automatically learning to tune the membership function parameters for mobile robot motion control. Moreover, the performance of these approaches are compared through simulation.

Keywords: Fuzzy Logic Controller, genetic algorithm, genetic-kuzzy algorithm, robotics, soft computing

Categories: I.2.3, I.2.9, L.2, L.5.0