A Deep Learning Tennis Ball Collection Robot and the Implementation on NVIDIA Jetson TX1 Board

Abstract

The service robots are becoming more and more popular in our daily life and bring us a lot of convenience. They also enter a few sports activities. For tennis playing, the tennis ball collection robot can relieve players' workload as people don't need to stoop to pick up tennis balls. There are two important tasks for a tennis ball collection robot. One is the recognition of tennis balls, and the other is the path planning of picking up balls. These two tasks can be fulfilled by machine learning algorithms. However, most conventional machine learning algorithms need hand-designed parameter, which are unable to design for these robotic tasks. Deep learning algorithms are base on nonlinear models which have great potential for these tasks. For this reason, we propose a tennis ball collection robot based on deep learning methods. For the path planning, we formulate it with the Travelling Salesman Problem (TSP) and then apply the Pointer Networks. And for tennis ball recognition, we use YOLO (You Only Look Once) model. These two models are implemented on NVIDIA Jetson TX1 board. With the proper training data set and training progress, these two models work well on the tennis ball collection robot.