Optimization and Analysis of Agricultural Robot Drive Systems

Abstract

This study takes the agricultural robot drive system as the research object and carries out optimization research from two dimensions: mechanical structure and intelligent control. At the mechanical level, redundant degrees of freedom are eliminated through topological analysis of mechanism freedom, a 'short-chain direct-drive' transmission scheme is introduced to reduce mechanical losses, and finite element topology optimization is combined to achieve lightweight design of key components. At the control level, artificial intelligence is introduced to establish a perception system based on convolutional neural networks (CNN) and multi-source information fusion, enabling accurate recognition and prediction of complex terrain. A Back Propagation (BP) neural network Proportion Integration Differentiation (PID) control strategy based on the Dung beetle Optimization (DBO)algorithm is proposed, which effectively addresses the shortcomings of traditional algorithms, such as slow convergence and large overshoot. Meanwhile, model predictive control and an improved soft actor-critic reinforcement learning algorithm are integrated to achieve online adaptive regulation of drive parameters and global optimal control to a considerable extent. The optimized drive system effectively improves the working performance and environmental adaptability of agricultural robots in complex terrain and provides new theoretical and technical ideas for the development of intelligent agricultural machinery.