摘要
[目的/意义]采摘机器人是智慧农业的重要组成部分,其感知、规划、控制相关基础方法理论目前已有系统化研究。然而,构建具备全果园“感知-移动-采摘”一体化作业能力的实用型采摘系统仍面临诸多挑战。针对该问题,本文调研并报道了本领域近期案例,将全果园自主作业的关键技术划分为局部目标感知、全局地图构建和自主作业行为规划三个子问题并进行综述。[进展]首先回顾了近距离、局部范围内水果目标的精细视觉感知方法,包括基于低级特征融合、高级特征学习、RGB-D信息融合,以及多视角信息融合的4种方法;介绍与分析了全局尺度下的果园地图构建与大规模场景视觉感知案例;在感知的基础上,调研分析采摘机器人自主作业行为规划方法,包括底盘移动路径规划、机械臂视点规划与避障路径规划等方面的最新研究;最后对采摘机器人自主作业系统构建案例进行报道与分析。[结论/展望]感知、移动、采摘模块的高效协同是实现采摘机器人从基础功能样机进一步迈向实用型机器的关键,已有的视觉感知、规划与控制算法的鲁棒性与稳定性均需增强,协同程度需进一步提高。此外,提及了采摘机器人应用的几个开放性研究问题,并描述了其未来发展趋势。
[Significance]Fruit-picking robot stands as a crucial solution for achieving intelligent fruit harvesting.Significant progress has been made in developing foundational methods for picking robots,such as fruit recognition,orchard navigation,path planning for picking,and robotic arm control,the practical implementation of a seamless picking system that integrates sensing,movement,and picking capabilities still encounters substantial technical hurdles.In contrast to current picking systems,the next generation of fruit-picking robots aims to replicate the autonomous skills exhibited by human fruit pickers.This involves effectively performing ongoing tasks of perception,movement,and picking without human intervention.To tackle this challenge,this review delves into the latest research methodologies and real-world applications in this field,critically assesses the strengths and limitations of existing methods and categorizes the essential components of continuous operation into three sub-modules:local target recognition,global mapping,and operation planning.[Progress]Initially,the review explores methods for recognizing nearby fruit and obstacle targets.These methods encompass four main approaches:low-level feature fusion,high-level feature learning,RGB-D information fusion,and multi-view information fusion,respectively.Each of these approaches incorporates advanced algorithms and sensor technologies for cluttered orchard environments.For example,low-level feature fusion utilizes basic attributes such as color,shapes and texture to distinguish fruits from backgrounds,while high-level feature learning employs more complex models like convolutional neural networks to interpret the contextual relationships within the data.RGB-D information fusion brings depth perception into the mix,allowing robots to gauge the distance to each fruit accurately.Multi-view information fusion tackles the issue of occlusions by combining data from multiple cameras and sensors around the robot,providing a more comprehensive view of the environment and enabling more reliable sensing.Subsequently,the review shifts focus to orchard mapping and scene comprehension on a broader scale.It points out that current mapping methods,while effective,still struggle with dynamic changes in the orchard,such as variations of fruits and light conditions.Improved adaptation techniques,possibly through machine learning models that can learn and adjust to different environmental conditions,are suggested as a way forward.Building upon the foundation of local and global perception,the review investigates strategies for planning and controlling autonomous behaviors.This includes not only the latest advancements in devising movement paths for robot mobility but also adaptive strategies that allow robots to react to unexpected obstacles or changes within the whole environment.Enhanced strategies for effective fruit picking using the Eye-in-Hand system involve the development of more dexterous robotic hands and improved algorithms for precisely predicting the optimal picking point of each fruit.The review also identifies a crucial need for further advancements in the dynamic behavior and autonomy of these technologies,emphasizing the importance of continuous learning and adaptive control systems to improve operational efficiency in diverse orchard environments.[Conclusions and Prospects]The review underscores the critical importance of coordinating perception,movement,and picking modules to facilitate the transition from a basic functional prototype to a practical machine.Moreover,it emphasizes the necessity of enhancing the robustness and stability of core algorithms governing perception,planning,and control,while ensuring their seamless coordination which is a central challenge that emerges.Additionally,the review raises unresolved questions regarding the application of picking robots and outlines future trends,include deeper integration of stereo vision and deep learning,enhanced global vision sampling,and the establishment of standardized evaluation criteria for overall operational performance.The paper can provide references for the eventual development of robust,autonomous,and commercially viable picking robots in the future.
作者
陈明猷
罗陆锋
刘威
韦慧玲
王金海
卢清华
骆少明
CHEN Mingyou;LUO Lufeng;LIU Wei;WEI Huiling;WANG Jinhai;LU Qinghua;LUO Shaoming(College of Mechanical and Electrical Engineering,Foshan University,Foshan 528231,China)
出处
《智慧农业（中英文）》
CSCD
2024年第5期20-39,共20页
Smart Agriculture
基金
国家自然科学基金项目(32301704,32171909)
广东省自然科学基金项目(2024A1515010199,2023A1515011255)。
关键词
采摘机器人
自主作业
局部感知
全局建图
行为规划
fruit picking robot
autonomous operation
local perception
global mapping
behavior planning
