Stroke Motion Learning for a Humanoid Robotic Ping-Pong Player Using a Novel Motion Capture System

Developing a vision module for a humanoid ping-pong game is challenging due to the spin and the non-linear rebound of the ping-pong ball. In this paper, we present a robust predictive vision module to overcome these problems. The hardware of the vision module is composed of two stereo camera pairs with each pair detecting the 3D positions of the ball on one half of the ping-pong table. The software of the vision module divides the trajectory of the ball into four parts and uses the perceived trajectory in the first part to predict the other parts. In particular, the software of the vision module uses an aerodynamic model to predict the trajectories of the ball in the air and uses a novel non-linear rebound model to predict the change of the ball's motion during rebound. The average prediction error of our vision module at the ball returning point is less than 50 mm -a value small enough for standard sized ping-pong rackets. Its average processing speed is 120fps. The precision and efficiency of our vision module enables two humanoid robots to play ping-pong continuously for more than 200 rounds.

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“…is larger than 0.4, we still use equations (19) and (20) to compute the linear velocity and angular velocity after rebound.…”

Section: The Rebound Model Of the Ping-pong Ballmentioning

confidence: 99%

A Robust Vision Module for Humanoid Robotic Ping-Pong Game

Chen

Huang

Wan

et al. 2015

International Journal of Advanced Robotic Systems

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“…The robot arm may be easier to be controlled for the stroke motion. Lai et al [14] used a humanoid robot with 7-DOF arms, which learns ping pong skills with motion capture system to strike the coming ball. Mülling et al [11] employed also a 7-DOF robot arm to return the ball to the opponent's court.…”

Section: Introductionmentioning

confidence: 99%

Ping-pong robotics with high-speed vision system

Wu²,

Lou

et al. 2012

2012 12th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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Abstract-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 multithreshold 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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