基于强化学习的农业机器人技术:应用、挑战与未来方向OA

With the rapid development of artificial intelligence,reinforcement learning(RL)has become an increasingly important technical route for transforming agricultural robots from rule-based control to learning-driven autonomy because of its capabilities in trial-and-error learning,sequential decision-making and environmental adaptation.It reviewed recent advances in RL-based agricultural robotics.It first introduced the basic concepts of RL,the Markov decision process,and representative algorithm families,including value-based methods,policy-gradient methods and actor-critic methods.It then summarized typical applications in autonomous navigation and path planning,single-arm fruit and vegetable harvesting,multi-arm collaborative harvesting and field operations,with particular attention to task modeling,training environments,hardware platforms and reported performance.Existing studies showed that RL can improve decision-making,trajectory optimization and operational adaptability under uncertain,dynamic and difficult-to-model agricultural conditions,thereby providing methodological support for precision agriculture and intelligent agricultural equipment.Nevertheless,practical deployment was still constrained by simulation-to-reality gaps,limited adaptability to unstructured terrain,insufficient safety verification and interpretability,immature multi-agent coordination mechanisms,and high equipment and maintenance costs.Future research should integrate digital twins,agricultural Internet of Things,multi-sensor perception,safe RL and modular robotic platforms to establish a closed loop among algorithm training,physical deployment and large-scale application,ultimately enabling reliable,scalable and cost-effective intelligent agricultural robotic systems in extensive farmland environments.