乒乓球机器人的视觉伺服系统

Real-time vision system for a Ping-Pong robot

视觉伺服的乒乓球机器人系统作为典型的"手眼系统",是研究高速视觉感知和快速伺服运动的理想平台,其涉及的高速物体识别跟踪、快速精确轨迹预测及机械臂伺服准确回球等关键技术在工业、军事等领域有广泛的应用前景.本文提出了乒乓球机器人的高速视觉伺服系统实现方法,包括基于特征直方图统计和快速轮廓搜索的目标识别算法,基于模型参数学习和自适应模型调整的物体运动状态估计和轨迹预测算法,及基于轨迹预测的灵巧臂回球规划算法.通过实验验证了各算法的实时性和高效性,并在165cm高的仿人机器人"悟"和"空"上成功实现了双机器人对打和与人对打任务.

As a typical real-time ＇eye-hand＇ system, the Ping-Pong robot is an ideal platform for high-speed visual perception and fast motor control. It involves high-speed object identification and tracking, trajectory prediction, and fast arm servo, which are also very significant in industrial and military field. This paper proposes a whole solution of real-time vision system for a ping-pong robot, including target recognition algorithm based on feature histogram analysis and fast contour search, modeling algorithm based on dynamics analysis, learning and training algorithms of model parameters, state estimating and trajectory predicting algorithms based on model adaption. To get more robust and accurate prediction under various serving conditions, the proposed method establishes two equivalent forms of the dynanfic model of flying ball, the discrete form for state estimation and the continuous form for trajectory prediction. These two forms share the same parameters. According to force analysis, the model parameters are deeply related to ball＇s state. So we trained the model parameters offline respect to ball＇s state, instead of setting them to constant values. Thus the model can be adapted accordingly online. Experimental results show the effectiveness and accuracy of the algorithms. It also works very well on Ping-Pong robots to rally with each other and with human.