Authors: JeongMin Choi SangJin Lee Mooncheol Won
Publish Date: 2011/03/12
Volume: 25, Issue: 1, Pages: 247-254
Abstract
Many mobile robot navigation methods use among others laser scanners ultrasonic sensors vision cameras for detecting obstacles and following paths However humans use only visual eg eye information for navigation In this paper we propose a mobile robot control method based on machine learning algorithms which use only camera vision To efficiently define the state of the robot from raw images our algorithm uses imageprocessing and feature selection steps to choose the feature subset for a neural network and uses the output of the neural network learned through supervised learning The output of the neural network uses the state of a reinforcement learning algorithm to learn obstacleavoiding and pathfollowing strategies using camera vision image The algorithm is verified by two experiments which are line tracking and obstacle avoidanceMooncheol Won received a BSc and an MSc degree from Seoul National University Korea in the Department of Naval Architecture and Ocean Engineering He also received a PhD degree in mechanical engineering from the University of California at Berkeley USA Currently he is a professor in the Department of Mechatronics Engineering at Chungnam National University Korea His research interests include control of maritime and mechatronics systems and machine learning applications of robotic systemsJeongMin Choi received a BSc degree Chungnam National University Korea in the Department of Mechatronics Engineering Currently he is in the researcher in the department of Research Center of Hyundai Wia Corp Korea His research interests include machine learning applications of robotic systems especially reinforcement learning and neural networksSangJin Lee received the BSc degree in the department of mechatronics engineering from Chungnam National University Korea He is in the master’s course in the department of mechatronics engineering of Chungnam National University Korea His research interests include machine learning applications of robotic systems
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