基于强化学习的农业机器人技术：应用、挑战与未来方向

Reinforcement Learning-based Agricultural Robotics: Applications, Challenges, and Future Directions

摘要:

随着人工智能技术的迅猛发展，强化学习（Reinforcement Learning, RL）因具备自主试错、序贯决策和环境自适应能力，正逐渐成为农业机器人从规则控制走向学习驱动的重要技术路径。本文围绕基于RL的农业机器人研究进展展开综述，首先介绍RL的基本概念、马尔可夫决策过程以及值函数、策略梯度和演员-评论家等主流算法类型;随后从自主导航与路径规划、单臂果蔬采摘、多臂协同采摘和田间处理等典型任务出发，系统梳理不同RL算法在农业场景中建模方式、训练环境、硬件平台和性能表现。综合已有研究可知，RL能在缺乏精确模型、环境动态变化和任务约束复杂条件下提升农业机器人的决策能力、路径优化能力和作业适应性，为精准农业与智能装备发展提供了新的方法支撑。然而，RL农业机器人仍面临仿真与现实迁移困难、非结构化地形适应性不足、安全性验证与可解释性不充分、多智能体协同机制尚不完善以及设备成本和运维压力较高等问题。未来研究需进一步融合数字孪生、农业物联网、多传感器感知、安全约束强化学习和模块化机器人平台，推动算法训练、实体部署和规模化应用之间形成闭环，构建高可靠、可扩展、低成本的农业智能作业系统，促进RL技术在大规模农田环境中实际落地。

Abstract:

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.