| dc.description.abstract | An outdoor automatic driving and navigation robot system is achieved in the thesis. The control system is implemented in an embedded development board Jetson TX1, along with a camera and a smartphone. Advanced technology such as deep learning, image processing, and motor control are combined to implement fuzzy-based robot system.
At the beginning of control flow, deep learning is utilized to analyze the images recorded by camera, so the robot is able to find the road regions and distinguish ordinary objects such as people and cars; particular characteristics are not required. Furthermore, custom smartphone application utilizes GPS and electronic compass sensors to get the position and direction of the robot as two information for navigation. Then, combining with Google Maps API, the smartphone application provides global route planning to the robot. Finally, navigation trajectory is computed via the combination of image recognition by deep learning and navigation information by smartphone application; therefore, robot is able to deal with traffic condition immediately and walk along the planned path. Fuzzy controllers for going straight and turning based on navigation trajectory are designed to complete entire robot control system.
Users could select destination via smartphone application; robot would automatically arrive demanded place according to the route defined by application and the result of deep learning after image processing.
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