Research on Autonomous Grasping of Humanoid Robots Based on Vision

With the advancement of robotics, humanoid robots have shown great application potential across various fields. This study focuses on autonomous grasping based on machine vision for humanoid robots, aiming to enhance their grasping adaptability and human-like movement capabilities in natural environments. In the machine vision module, a Realsense-D435 depth camera is adopted to collect object point cloud data, and the Iterative Closest Point (ICP) algorithm is used for point cloud registration to estimate object poses. The Denavit-Hartenberg (D-H) method models the robot's head, realizing coordinate transformation from the camera frame to the robot frame. For motion planning, referencing human arm grasping rules, the process is divided into nine basic movements, with tailored grasping poses for different objects to improve success rates. Remaining key points are autonomously calculated based on vision-derived grasping and placement points, using spatial arcs as trajectories. Matlab simulations verified the rationality of the end-effector and joint trajectories, followed by physical grasping experiments. Results show the robot can quickly and accurately recognize/locate objects in natural environments, completing grasping and carrying tasks with excellent performance: the grasping success rate reaches 89% for water bottles, 87% for small bottles, 87% for oranges, and 80% for bananas, with corresponding handling success rates of 87%, 82%, 82%, and 80%. The method ensures human-like movements while maintaining high adaptability, promoting the application of humanoid robots in daily life.