Ball-Catching System Using Image Processing and an Omni-Directional Wheeled Mobile Robot

The ball-catching system examined in this research, which was composed of an omni-directional wheeled mobile robot and an image processing system that included a dynamic stereo vision camera and a static camera, was used to capture a thrown ball. The thrown ball was tracked by the dynamic stereo vision camera, and the omni-directional wheeled mobile robot was navigated through the static camera. A Kalman filter with deep learning was used to decrease the visual measurement noises and to estimate the ball's position and velocity. The ball's future trajectory and landing point was predicted by estimating its position and velocity. Feedback linearization was used to linearize the omni-directional wheeled mobile robot model and was then combined with a proportional-integral-derivative (PID) controller. The visual tracking algorithm was initially simulated numerically, and then the performance of the designed system was verified experimentally. We verified that the designed system was able to precisely catch a thrown ball.

Automated summary

This research developed a system to catch thrown balls using an omni-directional wheeled mobile robot and image processing system. The dynamic stereo vision camera tracked the ball's movement, while a Kalman filter with deep learning was used to estimate the ball's position and velocity, successfully predicting its future trajectory. The system's performance was verified through numerical simulation and experimental validation.