Advances in Perception, Autonomous Operation, and Collaborative Systems for Smart Orchard Robots

Orchard production involves intensive labor requirements, limited operational periods, and highly dynamic and complex working environments. Consequently, the development of intelligent orchard robots has become a important approach to enhancing production efficiency and reducing reliance on manual operations. This review focuses on the demands of autonomous robotic systems operating in challenging orchard scenarios and provides a comprehensive overview of key technologies, including environmental perception and semantic cognition, autonomous navigation and environmental modeling, intelligent task execution, and collaborative robotic systems. Recent advances in fruit and blossom detection, branch and canopy structure perception, multi-modal sensor fusion for localization, semantic mapping, robotic harvesting control, variable-rate spraying, precision pollination, and autonomous intra-row weed management are systematically discussed. Furthermore, emerging technologies such as multi-robot coordination, robot–UAV cooperation, large language models (LLMs), and vision-language models (VLMs) for enhancing decision-making capabilities in agricultural robotics are reviewed. Finally, the existing challenges of orchard robots in terms of perception reliability, long-term autonomous navigation, operational robustness, system-level integration, and standardized performance evaluation are analyzed, followed by discussions on potential future research directions.