关键词: 农业机器人, 目标识别, 自主导航, 路径规划, 末端执行器, 智能控制

Abstract:

[Significance] Agricultural robots are increasingly expected to perform precise and timely operations under labor constraints while maintaining agronomic quality. Their operating conditions, however, differ substantially across open fields, orchards, greenhouses, and livestock facilities. Advances in a single algorithm or mechanism therefore do not necessarily translate into reliable operation of an integrated robot. This review examines how task requirements and environmental constraints shape the selection and integration of autonomous operation technologies. Particular attention is given to the relationships between component performance, complete task execution, and the interpretation of experimental evidence, with the aim of supporting equipment design and application decisions. [Progress] The review first distinguishes operation tasks from application environments. Seeding and transplanting, crop protection and weeding, selective harvesting, inspection and monitoring, and livestock operations require different outputs and evaluation criteria, whereas greenhouse production is treated as an environment that can accommodate several of these tasks. Representative research platforms and commercial systems illustrate alternative configurations, while product descriptions are distinguished from experimentally demonstrated performance. Wheeled, tracked, and legged chassis are compared in terms of steering geometry, terrain interaction, payload, maneuvering space, and maintenance demands. Modular configurations can improve adaptability, but changes in structure and load also require corresponding updates to calibration and control parameters. Lightweight design is considered together with traction, stiffness, stability, and tool requirements. Perception methods are examined according to the information needed for navigation and operation. Color and geometric processing, conventional machine learning, deep object detection, and semantic or instance segmentation offer different trade-offs in annotation requirements, computational demand, and sensitivity to changing conditions. Three-dimensional sensing must connect target observations to the robot and tool coordinate systems. Multimodal fusion can combine semantic, geometric, motion, and contact information, provided that calibration, synchronization, and degraded measurements are handled explicitly. Visual foundation models and domain adaptation offer opportunities to reduce labeling effort, but cross-field generalization and task-relevant errors remain essential considerations. Reduced supervision does not by itself establish readiness for crop-contact operation. Navigation, planning, and control are organized into five connected topics: multi-sensor localization and mapping, global and local path planning, coverage planning, manipulator planning and visual servoing, and autonomous control and decision-making. Absolute positioning, relative motion estimation, and path tracking are treated as distinct quantities. The discussion links global navigation satellite system (GNSS) availability, crop-row continuity, repetitive structures, and sensor limitations to localization choices. Path planning must account for vehicle kinematics, implement width, crop exclusion zones, headland access, and the consequences of obstacle avoidance for unfinished work. Manipulator operation further requires coordination of visibility, reachability, collision avoidance, and contact feedback. Research examples show why favorable results under continuous rows or simplified crop conditions require careful interpretation before application to more difficult environments. Execution technologies are analyzed through manipulator configuration, grasping and detachment, precision application, and coordination of contact and material handling. Representative experiments show that reported outcomes depend on crop arrangement, occlusion, positioning infrastructure, and treatment schedules. Accordingly, cross-task numerical rankings are avoided. System evaluation is developed from component testing through integrated closed-loop validation to trials in production environments. Reporting should identify experimental units, target populations, success rate denominators, variability, failure handling, and the time included in productivity estimates. Full operation cycles may include platform motion, retries, unloading, replenishment, and human assistance. Livestock operations require task-specific criteria rather than direct adoption of field-area productivity measures. [Conclusions and Prospects] Sustained agricultural autonomy requires technologies that fit agronomic conditions and work effectively together. Their combined performance needs to be assessed by the work completed to an acceptable standard. Priorities include reliable perception under changing conditions, accurate spatial alignment and time synchronization, feedback on operation quality, maintainable specialized components, and clear evaluation of reliability and cost. Foundation models, vision-language-action methods, synthetic data, and multi-robot collaboration offer further possibilities. Their practical value, however, requires independent testing of task completion, interaction with crops or animals, and recovery from failures. Economic assessments should state their scope and account for equipment utilization, human supervision, maintenance, and downtime. Trials at different locations and in different seasons, with comparable agronomic requirements and clearly recorded human involvement, would help establish whether research gains translate into dependable agricultural operation.

Key words: agricultural robots, target recognition, autonomous navigation, path planning, end effectors, intelligent control