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Smart Agriculture ›› 2024, Vol. 6 ›› Issue (5): 1-19.doi: 10.12133/j.smartag.SA202404006

• 综合研究 •    下一篇

设施农业机器人导航关键技术研究进展与展望

何勇1, 黄震宇1, 杨宁远1, 李禧尧1, 王玉伟2, 冯旭萍1,3()   

  1. 1. 浙江大学 生物系统工程与食品科学学院,浙江 杭州 310058,中国
    2. 安徽农业大学 工学院,安徽 合肥 230036,中国
    3. 浙江大学 新农村发展研究院,浙江 杭州 310058,中国
  • 收稿日期:2024-04-03 出版日期:2024-09-30
  • 基金项目:
    浙江大学“启真计划”项目2024年度中央高校基本科研业务费专项资金(226-2024-00038); 国家自然科学基金(32071895)
  • 作者简介:

    何 勇,研究方向为数字农业与精细农业、农业物联网技术、农业机械装备智能化检测与节能、管理信息系统与农用航空等。E-mail:

  • 通信作者:
    冯旭萍,博士,副研究员,研究方向为作物表型、利用作物多尺度的光谱图像信息结合深度学习等计算机算法实现作物表型的快速获取。E-mail:

Research Progress and Prospects of Key Navigation Technologies for Facility Agricultural Robots

HE Yong1, HUANG Zhenyu1, YANG Ningyuan1, LI Xiyao1, WANG Yuwei2, FENG Xuping1,3()   

  1. 1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
    2. College of Engineering, Anhui Agricultural University, Hefei 230036, China
    3. The Rural Development Academy, Zhejiang University, Hangzhou 310058, China
  • Received:2024-04-03 Online:2024-09-30
  • Foundation items:The Fundamental Research Funds for the Central Universities(226-2024-00038); National Natural Science Foundation of China(32071895)
  • About author:

    HE Yong, E-mail:

  • Corresponding author:
    FENG Xuping, E-mail:

摘要:

【目的/意义】 随着科学技术的快速发展和劳动力成本的不断提高,机器人在设施农业领域的应用越来越广泛。设施环境复杂多样,如何让机器人实现稳定、精准、快速地导航仍然是当前需要解决的问题。 【进展】 本文基于设施农业智能机器人的自动导航关键技术展开综述。在自主定位与地图构建方面,详细介绍了信标定位、惯性定位、即时定位与建图技术,以及融合定位方法。其中,依据使用的传感器不同,即时定位与建图技术可进一步划分为视觉、激光和融合三种不同类型。在全局路径规划方面,探讨了点到点局部路径规划和全局遍历路径规划在设施农业中的应用。针对规划目标数量的不同,详细介绍了单目标路径规划和多目标路径规划。此外,在机器人的自动避障技术方面,讨论了一系列设施农业中常用的避障控制算法。 【结论/展望】 总结了当前设施农业智能机器人自动导航技术面临的挑战,包括复杂环境、遮挡严重、成本高、作业效率低、缺乏标准化平台和公开数据集等问题。未来研究应重点关注多传感器融合、先进算法优化、多机器人协同作业,以及数据标准化与共享平台的建设。这些方向将有助于提升机器人在设施农业中的导航精度、效率和适应性,为智能农业的发展提供参考和建议。

关键词: 设施农业机器人, 导航, 定位, 路径规划, 避障

Abstract:

[Significance] With the rapid development of robotics technology and the persistently rise of labor costs, the application of robots in facility agriculture is becoming increasingly widespread. These robots can enhance operational efficiency, reduce labor costs, and minimize human errors. However, the complexity and diversity of facility environments, including varying crop layouts and lighting conditions, impose higher demands on robot navigation. Therefore, achieving stable, accurate, and rapid navigation for robots has become a key issue. Advanced sensor technologies and algorithms have been proposed to enhance robots' adaptability and decision-making capabilities in dynamic environments. This not only elevates the automation level of agricultural production but also contributes to more intelligent agricultural management. [Progress] This paper reviews the key technologies of automatic navigation for facility agricultural robots. It details beacon localization, inertial positioning, simultaneous localization and mapping (SLAM) techniques, and sensor fusion methods used in autonomous localization and mapping. Depending on the type of sensors employed, SLAM technology could be subdivided into vision-based, laser-based and fusion systems. Fusion localization is further categorized into data-level, feature-level, and decision-level based on the types and stages of the fused information. The application of SLAM technology and fusion localization in facility agriculture has been increasingly common. Global path planning plays a crucial role in enhancing the operational efficiency and safety of facility aricultural robots. This paper discusses global path planning, classifying it into point-to-point local path planning and global traversal path planning. Furthermore, based on the number of optimization objectives, it was divided into single-objective path planning and multi-objective path planning. In regard to automatic obstacle avoidance technology for robots, the paper discusses sevelral commonly used obstacle avoidance control algorithms commonly used in facility agriculture, including artificial potential field, dynamic window approach and deep learning method. Among them, deep learning methods are often employed for perception and decision-making in obstacle avoidance scenarios. [Conclusions and Prospects] Currently, the challenges for facility agricultural robot navigation include complex scenarios with significant occlusions, cost constraints, low operational efficiency and the lack of standardized platforms and public datasets. These issues not only affect the practical application effectiveness of robots but also constrain the further advancement of the industry. To address these challenges, future research can focus on developing multi-sensor fusion technologies, applying and optimizing advanced algorithms, investigating and implementing multi-robot collaborative operations and establishing standardized and shared data platforms.

Key words: facility agricultural robot, navigation, localization, path planning, obstacle avoidance

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