To control industry robots and make sure they are working in a correct status, an efficient way to judge the motion of the robot is important. In this article, an industry robotic motion and pose recognition method based on camera pose estimation and neural network are proposed. Firstly, industry robotic motion recognition based on the neural network has been developed to estimate and optimize motion of the robotics only by a monoscope camera. Secondly, the motion recognition including key flames recording and pose adjustment has been proposed and analyzed to restore the pose of the robotics more accurately. Finally, a KUKA industry robot has been used to test the proposed method, and the test results have demonstrated that the motion and pose recognition method can recognize the industry robotic pose accurately and efficiently without inertial measurement unit (IMU) and other censers. Below in the same algorithm, the error of the method introduced in this article is better than the traditional method using IMU and has a better merit of reducing cumulative error.

为了控制工业机器人并确保它们在正确的状态下工作，判断机器人运动的有效方法很重要。本文提出了一种基于相机姿态估计和神经网络的工业机器人运动姿态识别方法。首先，已经开发了基于神经网络的工业机器人运动识别，仅通过单镜相机来估计和优化机器人的运动。其次，提出并分析了包括关键火焰记录和姿态调整在内的运动识别，以更准确地恢复机器人的姿态。最后，使用 KUKA 工业机器人来测试所提出的方法，测试结果表明，该运动姿态识别方法无需惯性测量单元（IMU）和其他香炉，即可准确高效地识别工业机器人姿态。下面在同样的算法中，本文介绍的方法的误差优于使用IMU的传统方法，并且在减少累积误差方面具有更好的优点。