基于机器视觉的果树修剪机械臂运动控制研究OA

At present,fruit tree pruning still relies mainly on manual labor,which requires a large amount of labor input and is not very efficient.The pruning quality is also prone to fluctuations due to different operating experiences.In re-sponse to this situation,this article designs a fruit tree pruning robotic arm system and combines machine vision recogni-tion and motion control methods into the same workflow.The system uses deep learning methods to identify branch tar-gets,and then combines depth information to determine the spatial position of pruning nodes,which is used to describe the distribution structure of branches;In the control part of the robotic arm,the positioning process of the end effector is adjusted by combining inverse kinematics solution,trajectory interpolation,and improved PID and fuzzy adaptive control methods to improve motion accuracy and smooth operation.Considering the interweaving of branches,local occlusion,and narrow space within the orchard,the system has also added obstacle avoidance and posture correction functions.In the experiment,the overall operation results under different tree conditions were relatively stable,indicating that the sys-tem has certain adaptability and application foundation in the automation application of fruit tree pruning.