Tennis court mapping and trajectory optimization for robotic object retrieval / Eng Sheh Ling and Edgar Scavino

This paper presents a real-time tennis ball retrieval
system, using computer vision and a blind LEGO MINDSTORMS
EV3 robot. Our system tackles the tennis ball retrieval issue of
lacking ball boys, during professional or amateur matches. This
system is able to identify the location of a tennis ball in the whole
tennis court, to drive a robot along a fast trajectory while avoiding
collisions with players. Semantic information on the court is
captured using a webcam, which is located randomly near the
external border of the tennis court with a flexible height. The
image is then processed using a mapping algorithm to correlate
pixels in the image and points on the actual tennis court. The
camera location is evaluated by comparing the lines of the tennis
court in reality and in the image. Feature extraction and computer
vision techniques are used to detect the tennis ball and the player,
which are found with margins of 4.5 cm and 45 cm respectively.
To optimize the ball retrieval process, a genetic algorithm is
applied to define the optimal trajectory with fulfilling multiple
objectives for the robot to travel, arrive and retrieve a tennis ball.
MATLAB is used to process image extraction and processing, to
evaluate the mapping algorithm, and to define the trajectory
optimization on the image of the tennis court.