机器人在农业采摘中的应用与挑战
摘 要
随着全球人口增长和农业劳动力短缺问题日益突出，传统农业生产方式面临巨大挑战。本研究聚焦机器人技术在农业采摘领域的应用，旨在通过分析现有技术和实际应用情况，探索机器人采摘系统的技术瓶颈及解决方案。通过对国内外相关文献的系统梳理以及对典型应用场景的实地调研，发现当前机器人采摘技术主要集中在视觉识别、机械臂设计和路径规划三个方面。研究结果表明，基于深度学习的图像识别技术显著提高了果实检测精度，多自由度柔性机械臂的应用增强了采摘灵活性，而智能路径规划算法则有效提升了作业效率。然而，复杂环境适应性差、成本高昂以及能耗过高等问题仍然制约着该技术的大规模推广。本研究创新性地提出了一种融合多传感器信息的智能决策模型，能够根据作物生长状态动态调整采摘策略，实现了更高的采摘成功率和更低的误采率。此外，还开发了一套适用于不同地形条件下的轻量化移动平台，降低了设备整体能耗。
关键词：机器人采摘技术 机械臂设计 智能路径规划
Abstract
With the global population growth and the shortage of agricultural labor, the traditional agricultural production mode are facing great challenges. This study focuses on the application of robotics in the field of agricultural picking, aiming to explore the technical bottlenecks and solutions of the robot picking system by analyzing the existing technology and practical applications. Through the systematic combing of relevant literature at home and abroad and the field investigation of typical application scenarios, it is found that the current robot picking technology mainly focuses on three aspects: visual identification, robotic arm design and path planning. The results show that the image recognition technology based on deep learning significantly improves the accuracy of fruit detection, the application of multi-degree of freedom flexible mechanical arm enhances the flexibility of picking, and the intelligent path planning algorithm effectively improves the operation efficiency. However, the poor adaptability, high cost and high energy consumption of the complex environment still restrict the large-scale promotion of the technology. This study innovatively proposed an intelligent decision model integrating multi-sensor information, which can dynamically adjust the picking strategy according to the crop growth state, and achieve higher picking success rate and lower misharvest rate. In addition, a set of lightweight mobile platform suitable for different terrain conditions has been developed, reducing the overall energy consumption of the device.
Keyword:Robot Harvesting Technology Mechanical arm design Intelligent Path Planning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 1
2机器人采摘技术原理 2
2.1采摘机器人的机械结构 2
2.2感知与定位系统 2
2.3决策与控制系统 3
3机器人在农业采摘中的应用 4
3.1主要作物的采摘应用 4
3.2不同环境下的适应性 4
3.3提高采摘效率的方法 5
4面临的挑战与解决方案 6
4.1技术难题及应对 6
4.2成本效益分析 6
4.3社会伦理考量 7
结论 7
参考文献 9
致谢 10
摘 要
随着全球人口增长和农业劳动力短缺问题日益突出，传统农业生产方式面临巨大挑战。本研究聚焦机器人技术在农业采摘领域的应用，旨在通过分析现有技术和实际应用情况，探索机器人采摘系统的技术瓶颈及解决方案。通过对国内外相关文献的系统梳理以及对典型应用场景的实地调研，发现当前机器人采摘技术主要集中在视觉识别、机械臂设计和路径规划三个方面。研究结果表明，基于深度学习的图像识别技术显著提高了果实检测精度，多自由度柔性机械臂的应用增强了采摘灵活性，而智能路径规划算法则有效提升了作业效率。然而，复杂环境适应性差、成本高昂以及能耗过高等问题仍然制约着该技术的大规模推广。本研究创新性地提出了一种融合多传感器信息的智能决策模型，能够根据作物生长状态动态调整采摘策略，实现了更高的采摘成功率和更低的误采率。此外，还开发了一套适用于不同地形条件下的轻量化移动平台，降低了设备整体能耗。
关键词：机器人采摘技术 机械臂设计 智能路径规划
Abstract
With the global population growth and the shortage of agricultural labor, the traditional agricultural production mode are facing great challenges. This study focuses on the application of robotics in the field of agricultural picking, aiming to explore the technical bottlenecks and solutions of the robot picking system by analyzing the existing technology and practical applications. Through the systematic combing of relevant literature at home and abroad and the field investigation of typical application scenarios, it is found that the current robot picking technology mainly focuses on three aspects: visual identification, robotic arm design and path planning. The results show that the image recognition technology based on deep learning significantly improves the accuracy of fruit detection, the application of multi-degree of freedom flexible mechanical arm enhances the flexibility of picking, and the intelligent path planning algorithm effectively improves the operation efficiency. However, the poor adaptability, high cost and high energy consumption of the complex environment still restrict the large-scale promotion of the technology. This study innovatively proposed an intelligent decision model integrating multi-sensor information, which can dynamically adjust the picking strategy according to the crop growth state, and achieve higher picking success rate and lower misharvest rate. In addition, a set of lightweight mobile platform suitable for different terrain conditions has been developed, reducing the overall energy consumption of the device.
Keyword:Robot Harvesting Technology Mechanical arm design Intelligent Path Planning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法概述 1
2机器人采摘技术原理 2
2.1采摘机器人的机械结构 2
2.2感知与定位系统 2
2.3决策与控制系统 3
3机器人在农业采摘中的应用 4
3.1主要作物的采摘应用 4
3.2不同环境下的适应性 4
3.3提高采摘效率的方法 5
4面临的挑战与解决方案 6
4.1技术难题及应对 6
4.2成本效益分析 6
4.3社会伦理考量 7
结论 7
参考文献 9
致谢 10
