JUCS - Journal of Universal Computer Science 20(10): 1459-1468, doi: 10.3217/jucs-020-10-1459
Self-learning Mobile Robot Navigation in Unknown Environment Using Evolutionary Learning
Mohammed Algabri‡, Hassan Mathkour‡, Hedjar Ramdane‡, Mansour Alsulaiman‡, Khalid Al-Mutib‡‡ King Saud University, Riyadh, Saudi Arabia
Corresponding author: Mohammed Algabri ( m_gabri1400@yahoo.com ) © Mohammed Algabri, Hassan Mathkour, Hedjar Ramdane, Mansour Alsulaiman, Khalid Al-Mutib. This article is freely available under the J.UCS Open Content License. Citation:
Algabri M, Mathkour H, Ramdane H, Alsulaiman M, Al-Mutib K (2014) Self-learning Mobile Robot Navigation in Unknown Environment Using Evolutionary Learning. JUCS - Journal of Universal Computer Science 20(10): 1459-1468. https://doi.org/10.3217/jucs-020-10-1459 |  |
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
robotics, Fuzzy Logic Controller, soft computing, genetic algorithm, genetic-kuzzy algorithm