选择性收获机器人采收末端研究进展与展望

Research Progress and Prospects of Harvesting End Unit for Selective Harvesting Robots

摘要:

选择性收获机器人是实现农产品智能采收的重要技术途径，而由末端执行器、腕关节及其感知控制系统构成的采收末端，是决定采收成功率、损伤水平与环境适应能力的核心单元。针对当前相关研究中结构、感知与控制联系不够紧密的问题，本文面向果实、茶叶和花卉等不同采收对象，系统综述了选择性收获机器人采收末端的研究进展。首先，从采收对象生物特性、植株连接特性及冠层环境与采收空间约束出发，分析了采收末端的功能需求；其次，围绕末端执行器与腕关节结构，综述了不同接触方式末端、串联与并联腕关节构型及其适用特点；进一步总结了视觉感知、视触觉融合、关键部位识别、接触状态感知以及接近对准、柔顺抓取、分离转运控制等方面的研究现状；最后，从结构容错性、感知鲁棒性和控制自适应性等方面归纳了现存问题，并对模块化与可重构末端、多模态感知、农机农艺融合以及具身智能驱动的感知—决策—执行闭环采收发展趋势进行了展望。

Abstract:

Selective harvesting robots are an important technical approach for intelligent harvesting of agricultural products. The harvesting end, which consisted of the end-effector, wrist joint, and corresponding sensing and control system, was the key unit determining harvesting success rate, damage level, and environmental adaptability. In view of the insufficient coupling among structure, sensing, and control in current studies, the research progress of harvesting ends for selective harvesting robots targeting different objects, such as fruits, tea leaves, and flowers was systematically reviewed. Firstly, the functional requirements of harvesting ends were analyzed from the aspects of biological characteristics of harvesting objects, plant attachment characteristics, canopy environment, and local spatial constraints. Secondly, focusing on end-effector and wrist-joint structures, the structural characteristics and applicability of end-effectors with different contact modes and serial/ parallel wrist-joint configurations were reviewed. Furthermore, the research status of visual perception, visuotactile fusion, key-part recognition, contact-state perception, approach and alignment, compliant grasping, separation, and transfer control was summarized. Finally, the existing problems were discussed in terms of structural fault tolerance, sensing robustness, and control adaptability. Future development trends were proposed, including modular and reconfigurable harvesting ends, multimodal perception, integration of agricultural machinery and agronomy, and embodied-intelligence-driven closed-loop harvesting with perception, decision-making, and execution.