随着机器人技术的发展，仿人机器人在多个领域展现出应用潜力和价值。针对仿人机器人，基于机器视觉进行自主抓取研究，旨在提高仿人机器人在自然环境中的抓取适应能力和动作仿人性。在机器视觉方面，硬件采用Realsense-D435深度摄像头，利用YOLO（You Only Look Once）物体检测模型实现目标物体的识别、空间定位、深度图裁切和目标点云生成，并根据目标点云与标准点云的配准算法（ICP）获取物体的姿态，通过D-H法对机器人头部进行建模，将物体的位置和姿态由相机坐标系转换为机器人坐标系。在运动规划上，参照人手臂的抓取规律，将抓取过程分为9个基础动作：初始位、移动至预抓取位、抓取物体、提起物体、搬运物体、移动至放置位、放置物体、退出位和回到初始位，针对不同物体确定对应抓取姿态，以提高抓取成功率，根据视觉获取的抓取点和放置点，自主计算余下的关键点，并以空间弧形作为抓取轨迹，通过Matlab仿真，验证抓取过程机械臂末端运动轨迹和关节轨迹的合理性。最后进行物体抓取实验，结果表明，仿人机器人在自然环境中能够快速准确地识别和定位不同物体，并能成功进行抓取和搬运，成功率均在80%以上，并且能够兼顾动作的仿人性，验证了所提出方案的有效性。本研究可促进仿人机器人在人类日常生活中的应用和普及。

With the development of robotics technology, humanoid robots have shown application potential and value in multiple fields. Research on autonomous grasping of humanoid robots based on machine vision aims to improve their grasping adaptability and humanoid actions in natural environments. In terms of machine vision, the Realsense-D435 depth camera was adopted, and the YOLO (You Only Look Once) object detection model was used to achieve target object recognition, spatial positioning, depth map cropping, and target point cloud generation. The object’s posture was obtained based on the registration algorithm (ICP) between the target point cloud and the standard point cloud. The robot head was modeled using the D-H method, and the position and posture of the object were converted from the camera coordinate system to the robot coordinate system. In terms of motion planning, according to the grasping law of the human arm, the grasping process was divided into 9 basic actions: initial position, moving to the pre-grasping position, grasping the object, lifting the object, moving the object, moving to placement position placing the object, retreating position, and returning to the initial position. Corresponding grasping postures were determined for different objects to improve the success rate of grasping. Based on the grasping and placing points obtained visually, the remaining key points were calculated independently, and the spatial arc was used as the grasping trajectory. Through Matlab simulation, the rationality of the end movement trajectory and joint trajectory of the robotic arm during the grasping process was verified. Finally, an object grasping experiment was conducted, and the results showed that the humanoid robot can quickly and accurately recognize and locate different objects in the natural environment, and can successfully grasp and transport them with a success rate of over 80%. And it takes into account the imitation of human nature of the action, verifying the effectiveness of the proposed solution. This study can promote the application and popularization of humanoid robots in human daily life.