Intelligent Robotic Arm Grasping System for the Agricultural Sector

Abstract

In order to promote the intelligent transformation of China's agricultural picking process and solve the development problem of insufficient mechanization level in the apple industry, this article takes the agricultural intelligent robotic arm grasping system as the research object and systematically elaborates on its key technical system. The article analyzes the application value of technology based on the actual needs of the industry, and at the same time, identifies the shortcomings of existing research, providing direction for optimizing intelligent picking equipment. In terms of core technology, the improved YOLOv7 algorithm has improved the accuracy of apple recognition, and binocular vision technology has achieved high-precision positioning. The four-finger underactuated robotic arm can adaptively grasp apples of different sizes, and the fusion system of model predictive control (MPC) and support vector classification (SVC) ensures lossless grasping efficiency. Through practical application verification, the success rate of system picking and the efficiency of single fruit operation have shown good performance. This article focuses on the collaborative analysis of technical modules, with a particular emphasis on the adaptation of low-rootstock dense planting orchard scenarios. It integrates technical indicators throughout the entire process to address research fragmentation issues, and also explores existing challenges and future development directions, such as environmental adaptability, providing a systematic reference for optimizing intelligent robotic arm grasping systems.