Autonomous positioning control of manipulator and fast surface fitting based on particle filter and point cloud library technology

The real-time calculations of the positioning error, error correction, and state analysis have always been a difficult challenge in the process of autonomous positioning. In order to solve this problem, a simple depth imaging equipment (Kinect) is used, and a particle filter based on three-frame subtraction to capture the end-effector’s motion is proposed in this article. Further, a back-propagation neural network is adopted to recognize targets. The point cloud library technology is used to collect the space coordinates of the end-effector and target. Finally, a three-dimensional mesh simplification algorithm based on the density analysis and average distance between points is proposed to carry out data compression. Accordingly, the target point cloud is fitted quickly. The experiments conducted in the article demonstrate that the proposed algorithm can detect and track the end-effector in real time. The recognition rate of 99% is achieved for a cylindrical object. The geometric center of all particles is regarded as the end-effector’s center. Furthermore, the gradual convergence of the end-effector center to the target centroid shows that the autonomous positioning is successful. Compared to traditional algorithms, both moving the end-effector and a stationary object can be extracted from image frames using a thesis. The thesis presents a simple and convenient positioning method, which adjusts the motion of the manipulator according to the error between the end-effector’s center and target centroid. The computational complexity is reduced and the camera calibration is eliminated.