人与机器人共存中的位姿估计与碰撞检测

提出了一种人与机器人共存中的位姿估计与碰撞检测方法。首先，利用光学3维动作捕捉系统获取标记点位姿信息，建立人体手臂的运动学模型。其次，针对工作空间中障碍物遮挡导致部分标记点位姿信息丢失的问题，将角度传感器获取的肘关节角度作为人体手臂运动学模型的输入，获取人体手臂末端位姿信息。再次，构建人体手臂和协作机器人的胶囊体模型，计算各胶囊体之间的最短距离，从而判断人机的相对位姿关系并实现碰撞检测。最后，通过10个人在不同人机共存场景下对人机位姿估计与碰撞检测方法进行评价。实验结果表明，本方法估计的人体手臂末端位置误差在20mm以内，人机最短距离的最大误差为14.53mm，能够实现人机碰撞检测。

Pose Estimation and Collision Detection in Human-robot Coexistence

A method of pose estimation and collision detection in human-robot coexistence is proposed. Firstly, an optical three-dimensional motion capture system is used to obtain the pose information of mark points to establish a kinematic model of the human arm. Secondly, in order to solve the problem that the pose information of some mark points is lost due to obstacles in the workspace, the elbow joint angle obtained by the angle sensor is used as the input of the human arm kinematics model to obtain the pose information of the end of the human arm. Thirdly, the capsule model of the human arm and the collaborative robot is established, and the shortest distance among the capsules is calculated, so as to judge the relative pose relationship between the human and the machine and realize the collision detection. Finally, 10 persons are used to evaluate the human-machine pose estimation and collision detection methods in different human-machine coexistence scenarios. The experimental results show that the error of the end position of the human arm estimated by the proposed method is within 20mm, and the maximum error of the shortest distance between the human and the machine is 14.53mm, which is sufficient for man-machine collision detection.