基于Kinect动态手势识别的机械臂实时位姿控制系统

基于Kinect动态手势识别达到实时控制机械臂末端位姿的效果。位置控制信息的获取采用Kinect计算手部4个关节点在控制中的位置变动,数据噪声在控制中易引起机械臂误动作和运动振动等问题,为了避免噪声对实时控制的不利影响,采用卡尔曼滤波跟踪降噪。姿势控制信息通过采集手部点云经滤波处理后应用最小二乘拟合的方式获取掌心所在平面,运用迭代器降噪处理。系统通过对手部位置和姿势信息的整合、手势到机械臂空间坐标映射及运动学求解来实时控制机械臂末端位姿。实验结果证明,手势控制系统满足控制要求,简单、易于操作,机械臂实时响应速度快、运动准确。

Real-time Mechanical Arm Position and Pose Control System by Dynamic Hand Gesture Recognition Based on Kinect Device

The research achieved to control the mechanical arm position and pose by using real-time dynamic gesture recognition based on Kinect device. The information of the position controlling was obtained by calculating the position changes of the four hand joint points. The noise of the joints was liable to lead mechanical arm misoperation and the vibration of motion during the control of the mechanical arm. Aiming to avoid the negative impact of the noise in real-time controlling, Kalman filter was adopted to track position and reduce noise. According to the hand point cloud information, the information of the posture controlling was obtained by means of using least squares fitting to get the plane of hand mind. The end of the position and pose of the mechanical arm was controlled by integrating the position and posture information, space coordinate mapping and the resolving of kinematics in real-time. The result of the experiment indicated that the gesture control was easy to operate and mechanical arm responded at high speed. The effect of filter was so remarkable that the motion of the mechanical arm was controlled accurately and smoothly, and no mechanical arm misoperation and others controlling anomaly. Gesture control system could meet the requirement of actually controlling. System could be applied to a variety of human-computer interaction.