The performance of vision-based control is usually limited by the low sampling rate of the visual feedback. We address Ping-Pong robotics as a widely studied example which requires high-speed vision for highly dynamic motion control. In order to detect a flying ball accurately and robustly, a multi-threshold segmentation algorithm is applied in a stereo-vision running at 150Hz. Based on the estimated 3D ball positions, a novel two-phase trajectory prediction is exploited to determine the hitting position. Benefiting from the high-speed visual feedback, the hitting position and thus the motion planning of the manipulator are updated iteratively with decreasing error. Experiments are conducted on a 7 degrees of freedom humanoid robot arm. A successful Ping-Pong playing between the robot arm and human is achieved with a high successful rate of 88%.
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