Industry robotic motion and pose recognition method based on camera pose estimation and neural network

Abstract

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.