Self-learning navigation algorithm for vision-based mobile robots using machine learning algorithms

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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.