Self-learning navigation algorithm for vision-based mobile robots using machine learning algorithms |
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Authors: | Jeong-Min Choi Sang-Jin Lee and Mooncheol Won |
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Affiliation: | (1) School of Mechanical Engineering, Yanshan University, 066004 Qinhuangdao, China;(2) School of Information Science and Engineering, Yanshan University, 066004 Qinhuangdao, China |
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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 (e.g. 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 image-processing 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 obstacle-avoiding and path-following strategies using camera
vision image. The algorithm is verified by two experiments, which are line tracking and obstacle avoidance. |
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Keywords: | |
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