无人驾驶拖拉机田间作业性能优化分析 -农业机械化及其自动化专业

摘要

随着精准农业技术的快速发展，无人驾驶拖拉机作为现代农业装备的重要组成部分，其田间作业性能直接影响农业生产效率与质量。本研究针对当前无人驾驶拖拉机在复杂农田环境中存在的路径规划精度不足、作业稳定性欠佳等问题，提出了一种基于多传感器融合的智能优化方法。通过整合北斗定位系统、惯性测量单元和视觉传感器等多源数据，构建了高精度的环境感知模型；采用改进的算法与模糊控制理论相结合的方式，实现了动态路径规划与实时纠偏；同时引入深度强化学习算法，优化了拖拉机的自主决策能力。研究选取中国东北地区典型农田进行实地测试，结果表明：与传统方法相比，优化后的系统在直线行驶精度上提升了35%，转弯半径误差降低了42%，作业效率提高了28%。此外，系统在应对突发障碍物时的响应时间缩短至0.8秒以内，显著增强了安全性与适应性。

关键词：无人驾驶拖拉机；多传感器融合；路径规划

OPTIMIZATION ANALYSIS OF FIELD OPERATION PERFORMANCE OF UNMANNED TRACTOR

ABSTRACT

With the rapid development of precision agriculture technology, driverless tractors are an important part of modern agricultural equipment, and their field performance directly affects the efficiency and quality of agricultural production. In this paper, an intelligent optimization method based on multi-sensor fusion is proposed to solve the problems of path planning accuracy and stability of unmanned tractors in complex farmland environment. By integrating multi-source data such as Beidou positioning system, inertial measurement unit and vision sensor, a high-precision environment perception model is constructed. By combining the improved algorithm with fuzzy control theory, dynamic path planning and real-time deviation correction are realized. At the same time, a deep reinforcement learning algorithm is introduced to optimize the autonomous decision-making ability of the tractor. The results show that compared with the traditional method, the optimized system improves the linear driving accuracy by 35%, reduces the turning radius error by 42%, and increases the operating efficiency by 28%. In addition, the system's response time to sudden obstacles is reduced to less than 0.8 seconds, significantly enhancing safety and adaptability.

KEY WORDS：Driverless Tractor; Multi-Sensor Fusion; Path Planning

摘要 I

ABSTRACT II

第1章绪论 2

1.1 研究背景及意义 2

1.2 无人驾驶拖拉机田间作业性能优化研究现状 2

第2章无人驾驶拖拉机田间作业性能影响因素分析 4

2.1 环境因素对作业性能的影响 4

2.2 机械结构与控制系统的优化需求 4

2.3 导航与路径规划技术的性能提升 5

第3章无人驾驶拖拉机田间作业性能优化策略 6

3.1 基于深度学习的自适应控制方法 6