Abstract
With the development of artificial intelligence, the utilization of robots based on AI is widespread in our daily life, especially in the area of sports. In the aspect of tennis, collecting tennis balls on the ground after a fierce match or training would be tiresome work, so an automatic tennis ball picking robot becomes useful. Three main aspects should be considered in the research of the tennis ball collection robot: the recognition and localization of tennis balls, path planning for collecting every tennis ball, and the global positioning and navigation of the robot. Firstly, computer vision based on deep learning algorithms has excellent reliability, and the MobileNet-SSD model can be quantized and deployed on Raspberry Pi. Therefore, we choose the MobileNet-SSD model with a monocular camera catching pictures to recognize tennis balls. Secondly, perspective transformation is used to get the precise location of the target tennis ball. We propose a regional traversal algorithm to plan the path to collect as many tennis balls as possible. Thirdly, we utilize ultra-wide-band (UWB) supplemented by triangle centroid methods to locate the robot in a global position. After proper training, the tennis ball collection robot performs well and has excellent potential.
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