基于深度强化学习与条件动作树的精准农业导航与化学喷洒优化
Optimizing Navigation And Chemical Application in Precision Agriculture With Deep Reinforcement Learning And Conditional Action Tree
AI总结：
提出基于分层动作掩码的强化学习框架HAM-PPO，用于精准农业机器人导航与化学喷洒优化，在有限电量下提高感染区域覆盖率并减少药剂使用，实验证明其优于基线方法。
摘要：
本文提出了一种新颖的基于强化学习（RL）的规划方案，用于精准农业中生物胁迫的优化机器人管理。该框架采用具有条件动作掩码的分层决策结构，其中高层动作引导机器人的探索，而低层动作优化其在受影响区域的导航和高效化学喷洒。优化的关键目标包括在有限电池电量下提高感染区域的覆盖率以及减少化学药剂使用量，从而避免对田间健康区域进行不必要的喷洒。我们的数值实验结果表明，所提出的方法——分层动作掩码近端策略优化（HAM-PPO）——在产量恢复和资源效率方面显著优于基线实践，例如割草机式导航+无差别喷洒（地毯式喷洒）。HAM-PPO在各种感染场景下持续实现更高的产量恢复百分比和更低的化学药剂成本。该框架还表现出对观测噪声的鲁棒性以及在多样环境条件下的泛化能力，适应不同的感染范围和空间分布模式。
英文摘要：
This paper presents a novel reinforcement learning (RL)-based planning scheme for optimized robotic management of biotic stresses in precision agriculture. The framework employs a hierarchical decision-making structure with conditional action masking, where high-level actions direct the robot's exploration, while low-level actions optimize its navigation and efficient chemical spraying in affected areas. The key objectives of optimization include improving the coverage of infected areas with limited battery power and reducing chemical usage, thus preventing unnecessary spraying of healthy areas of the field. Our numerical experimental results demonstrate that the proposed method, Hierarchical Action Masking Proximal Policy Optimization (HAM-PPO), significantly outperforms baseline practices, such as LawnMower navigation + indiscriminate spraying (Carpet Spray), in terms of yield recovery and resource efficiency. HAM-PPO consistently achieves higher yield recovery percentages and lower chemical costs across a range of infection scenarios. The framework also exhibits robustness to observation noise and generalizability under diverse environmental conditions, adapting to varying infection ranges and spatial distribution patterns.
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