摘 要
随着农业现代化的快速发展，智能农机装备成为提升农业生产效率和精准化水平的关键技术之一。本研究旨在设计并实现一种基于多传感器融合的智能拖拉机自动驾驶系统，以满足复杂农田环境下的高精度作业需求。通过集成全球导航卫星系统（GNSS）、惯性导航系统（INS）以及视觉传感器，构建了鲁棒性强、适应性广的定位与感知模块，并采用改进的路径规划算法优化拖拉机在不同地形条件下的行驶轨迹。同时，为解决传统控制系统在动态环境中的响应滞后问题，引入深度强化学习方法对自动驾驶控制器进行训练，显著提升了系统的实时性和稳定性。实验结果表明，该系统能够在多种典型农田场景中实现厘米级定位精度和亚秒级控制延迟，相较于现有方案表现出更优的作业性能。此外，本研究提出的多模态数据融合框架有效降低了单一传感器失效对整体系统的影响，增强了系统的可靠性。总体而言，本研究不仅为智能拖拉机的自动驾驶技术提供了创新性解决方案，还为未来智慧农业的发展奠定了重要基础。
关键词：智能拖拉机；多传感器融合；自动驾驶系统；路径规划算法；深度强化学习
Design and Implementation of an Autonomous Driving System for Intelligent Tractors
ABSTRACT
With the rapid development of agricultural modernization, intelligent agricultural machinery has become one of the key technologies for enhancing the efficiency and precision of agricultural production. This study aims to design and implement a multi-sensor fusion-based autonomous driving system for intelligent tractors to meet the high-precision operation requirements in complex farmland environments. By integrating Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), and visual sensors, a robust and adaptable localization and perception module was constructed. An improved path planning algorithm was adopted to optimize the tractor's trajectory under various terrain conditions. To address the response lag issues of traditional control systems in dynamic environments, a deep reinforcement learning method was introduced to train the autonomous driving controller, significantly improving the system's real-time performance and stability. Experimental results demonstrate that the system can achieve centimeter-level positioning accuracy and sub-second control latency in multiple typical farmland scenarios, exhibiting superior operational performance compared to existing solutions. Additionally, the multimodal data fusion fr amework proposed in this study effectively reduces the impact of single-sensor failure on the overall system, enhancing its reliability. Overall, this research not only provides an innovative solution for the autonomous driving technology of intelligent tractors but also lays an important foundation for the future development of smart agriculture.
KEY WORDS：Intelligent Tractor；Multi-Sensor Fusion；Autonomous Driving System；Path Planning Algorithm；Deep Reinforcement Learning
目 录
摘 要 I
ABSTRACT II
第一章 绪论 1
1.1 智能拖拉机自动驾驶的研究背景 1
1.2 系统设计与实现的意义分析 1
1.3 国内外研究现状综述 1
1.4 本文研究方法与技术路线 2
第二章 系统需求分析与总体设计 2
2.1 智能拖拉机自动驾驶的功能需求 2
2.2 系统架构设计与模块划分 2
2.3 关键技术难点分析 3
2.4 总体设计方案评估 3
第三章 核心模块设计与算法实现 4
3.1 导航定位模块的设计与优化 4
3.2 路径规划算法的实现与验证 4
3.3 自动控制模块的开发与测试 5
3.4 数据融合技术的应用研究 5
第四章 系统集成与性能测试 6
4.1 系统硬件平台搭建与调试 6
4.2 软件系统集成与功能验证 6
4.3 实验环境设置与测试方案设计 7
4.4 测试结果分析与性能评价 7
结 论 8
参考文献 9
致 谢 10
随着农业现代化的快速发展，智能农机装备成为提升农业生产效率和精准化水平的关键技术之一。本研究旨在设计并实现一种基于多传感器融合的智能拖拉机自动驾驶系统，以满足复杂农田环境下的高精度作业需求。通过集成全球导航卫星系统（GNSS）、惯性导航系统（INS）以及视觉传感器，构建了鲁棒性强、适应性广的定位与感知模块，并采用改进的路径规划算法优化拖拉机在不同地形条件下的行驶轨迹。同时，为解决传统控制系统在动态环境中的响应滞后问题，引入深度强化学习方法对自动驾驶控制器进行训练，显著提升了系统的实时性和稳定性。实验结果表明，该系统能够在多种典型农田场景中实现厘米级定位精度和亚秒级控制延迟，相较于现有方案表现出更优的作业性能。此外，本研究提出的多模态数据融合框架有效降低了单一传感器失效对整体系统的影响，增强了系统的可靠性。总体而言，本研究不仅为智能拖拉机的自动驾驶技术提供了创新性解决方案，还为未来智慧农业的发展奠定了重要基础。
关键词：智能拖拉机；多传感器融合；自动驾驶系统；路径规划算法；深度强化学习
Design and Implementation of an Autonomous Driving System for Intelligent Tractors
ABSTRACT
With the rapid development of agricultural modernization, intelligent agricultural machinery has become one of the key technologies for enhancing the efficiency and precision of agricultural production. This study aims to design and implement a multi-sensor fusion-based autonomous driving system for intelligent tractors to meet the high-precision operation requirements in complex farmland environments. By integrating Global Navigation Satellite System (GNSS), Inertial Navigation System (INS), and visual sensors, a robust and adaptable localization and perception module was constructed. An improved path planning algorithm was adopted to optimize the tractor's trajectory under various terrain conditions. To address the response lag issues of traditional control systems in dynamic environments, a deep reinforcement learning method was introduced to train the autonomous driving controller, significantly improving the system's real-time performance and stability. Experimental results demonstrate that the system can achieve centimeter-level positioning accuracy and sub-second control latency in multiple typical farmland scenarios, exhibiting superior operational performance compared to existing solutions. Additionally, the multimodal data fusion fr amework proposed in this study effectively reduces the impact of single-sensor failure on the overall system, enhancing its reliability. Overall, this research not only provides an innovative solution for the autonomous driving technology of intelligent tractors but also lays an important foundation for the future development of smart agriculture.
KEY WORDS：Intelligent Tractor；Multi-Sensor Fusion；Autonomous Driving System；Path Planning Algorithm；Deep Reinforcement Learning
目 录
摘 要 I
ABSTRACT II
第一章 绪论 1
1.1 智能拖拉机自动驾驶的研究背景 1
1.2 系统设计与实现的意义分析 1
1.3 国内外研究现状综述 1
1.4 本文研究方法与技术路线 2
第二章 系统需求分析与总体设计 2
2.1 智能拖拉机自动驾驶的功能需求 2
2.2 系统架构设计与模块划分 2
2.3 关键技术难点分析 3
2.4 总体设计方案评估 3
第三章 核心模块设计与算法实现 4
3.1 导航定位模块的设计与优化 4
3.2 路径规划算法的实现与验证 4
3.3 自动控制模块的开发与测试 5
3.4 数据融合技术的应用研究 5
第四章 系统集成与性能测试 6
4.1 系统硬件平台搭建与调试 6
4.2 软件系统集成与功能验证 6
4.3 实验环境设置与测试方案设计 7
4.4 测试结果分析与性能评价 7
结 论 8
参考文献 9
致 谢 10
