Research on Robot Precise Grasping Control Algorithm Based on Visual Servoing

This study focuses on the robot's precise grasping control algorithm based on visual servo, and proposes an efficient control method that adjusts the position and posture errors through real-time visual feedback to achieve high-precision grasping of target objects of different shapes and sizes in complex scenes. The algorithm is based on RGB-D image information. By mapping the depth information of the two-dimensional image into three-dimensional space to generate point cloud data, the robot can accurately identify the spatial position and posture of the object, and dynamically adjust it according to real-time feedback, thereby ensuring the accuracy and stability of the grasping operation. The experimental results show that the designed control algorithm exhibits a high grasping success rate and error convergence speed in a variety of complex environments, and after multiple iterations of training, the grasping efficiency and execution time are significantly optimized. Model verification further proves the effective control of the algorithm on position error and posture error, providing practical technical support and theoretical reference for the realization of autonomous grasping control of robots in dynamic environments, and has broad application potential.