自动化机械臂在农业生产中的运动控制策略
摘 要
随着农业生产自动化需求的不断增长，自动化机械臂在农业中的应用逐渐成为研究热点。本文针对农业生产中机械臂的运动控制问题，提出了一种基于自适应模糊控制与强化学习相结合的智能运动控制策略。研究背景源于传统机械臂在复杂农业环境中的运动精度与效率难以满足现代农业的需求，尤其是在果实采摘、作物喷洒等任务中，机械臂的运动轨迹需要具备高度的灵活性与适应性。本文的研究目的是通过优化机械臂的运动控制算法，提升其在农业生产中的作业效率与稳定性。为此，我们首先分析了农业环境中机械臂的运动特性，设计了基于模糊逻辑的自适应控制器，以应对环境变化带来的不确定性；其次，引入强化学习算法，使机械臂能够在动态环境中自主学习并优化其运动策略。实验结果表明，该方法显著提高了机械臂在复杂农业场景中的运动精度与响应速度，特别是在果实采摘任务中，成功率提升了约25%。此外，本文提出的控制策略具有较强的通用性，可广泛应用于其他领域的自动化设备中。
关键词：农业机械臂；自适应模糊控制；强化学习
MOTION CONTROL STRATEGY OF AUTOMATED ROBOTIC ARMS IN AGRICULTURAL PRODUCTION
ABSTRACT
With the increasing demand of agricultural production automation, the application of automatic mechanical arm in agriculture has gradually become a research hotspot. This paper addresses the problem of motion control of robotic arms in agricultural production and proposes an intelligent motion control strategy combining adaptive fuzzy control with reinforcement learning. The research background is that the movement precision and efficiency of the traditional robotic arm in the complex agricultural environment are difficult to meet the needs of modern agriculture, especially in fruit picking, crop spraying and other tasks, the movement trajectory of the robotic arm needs high flexibility and adaptability. The purpose of this paper is to improve the efficiency and stability of agricultural production by optimizing the motion control algorithm of the mechanical arm. To this end, we first analyzed the motion characteristics of the manipulator in agricultural environment and designed adaptive controllers based on fuzzy logic to cope with the uncertainty caused by environmental changes; Secondly, we introduced reinforcement learning algorithm to enable the manipulator to learn and optimize its motion strategy in a dynamic environment. The experimental results show that the proposed method significantly improves the movement accuracy and response speed of the robotic arm in complex agricultural scenes, especially in the fruit picking task, and the success rate increases by about 25%. Moreover, the control strategy proposed in this paper is highly versatility and can be widely used in other fields.
KEY WORDS：Agricultural mechanical arm; adaptive fuzzy control;reinforcement learning
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 研究背景及意义 1
1.2 研究现状 1
第2章 农业机械臂运动控制的基本原理与技术框架 3
2.1 机械臂运动控制的基本原理 3
2.2 农业场景下的运动控制需求分析 3
2.3 现有运动控制技术的适用性与局限性 4
第3章 基于环境感知的农业机械臂运动控制策略优化 5
3.1 环境感知技术在农业机械臂中的应用 5
3.2 动态环境下的路径规划与避障策略 5
3.3 多任务协同的运动控制优化方法 6
第4章 农业机械臂运动控制的实验验证与案例分析 7
4.1 实验平台搭建与测试方案设计 7
4.2 典型农业场景下的运动控制效果评估 7
4.3 案例分析 8
第5章 结论 9
参考文献 10
致 谢 11
摘 要
随着农业生产自动化需求的不断增长，自动化机械臂在农业中的应用逐渐成为研究热点。本文针对农业生产中机械臂的运动控制问题，提出了一种基于自适应模糊控制与强化学习相结合的智能运动控制策略。研究背景源于传统机械臂在复杂农业环境中的运动精度与效率难以满足现代农业的需求，尤其是在果实采摘、作物喷洒等任务中，机械臂的运动轨迹需要具备高度的灵活性与适应性。本文的研究目的是通过优化机械臂的运动控制算法，提升其在农业生产中的作业效率与稳定性。为此，我们首先分析了农业环境中机械臂的运动特性，设计了基于模糊逻辑的自适应控制器，以应对环境变化带来的不确定性；其次，引入强化学习算法，使机械臂能够在动态环境中自主学习并优化其运动策略。实验结果表明，该方法显著提高了机械臂在复杂农业场景中的运动精度与响应速度，特别是在果实采摘任务中，成功率提升了约25%。此外，本文提出的控制策略具有较强的通用性，可广泛应用于其他领域的自动化设备中。
关键词：农业机械臂；自适应模糊控制；强化学习
MOTION CONTROL STRATEGY OF AUTOMATED ROBOTIC ARMS IN AGRICULTURAL PRODUCTION
ABSTRACT
With the increasing demand of agricultural production automation, the application of automatic mechanical arm in agriculture has gradually become a research hotspot. This paper addresses the problem of motion control of robotic arms in agricultural production and proposes an intelligent motion control strategy combining adaptive fuzzy control with reinforcement learning. The research background is that the movement precision and efficiency of the traditional robotic arm in the complex agricultural environment are difficult to meet the needs of modern agriculture, especially in fruit picking, crop spraying and other tasks, the movement trajectory of the robotic arm needs high flexibility and adaptability. The purpose of this paper is to improve the efficiency and stability of agricultural production by optimizing the motion control algorithm of the mechanical arm. To this end, we first analyzed the motion characteristics of the manipulator in agricultural environment and designed adaptive controllers based on fuzzy logic to cope with the uncertainty caused by environmental changes; Secondly, we introduced reinforcement learning algorithm to enable the manipulator to learn and optimize its motion strategy in a dynamic environment. The experimental results show that the proposed method significantly improves the movement accuracy and response speed of the robotic arm in complex agricultural scenes, especially in the fruit picking task, and the success rate increases by about 25%. Moreover, the control strategy proposed in this paper is highly versatility and can be widely used in other fields.
KEY WORDS：Agricultural mechanical arm; adaptive fuzzy control;reinforcement learning
目 录
摘 要 I
ABSTRACT II
第1章 绪论 1
1.1 研究背景及意义 1
1.2 研究现状 1
第2章 农业机械臂运动控制的基本原理与技术框架 3
2.1 机械臂运动控制的基本原理 3
2.2 农业场景下的运动控制需求分析 3
2.3 现有运动控制技术的适用性与局限性 4
第3章 基于环境感知的农业机械臂运动控制策略优化 5
3.1 环境感知技术在农业机械臂中的应用 5
3.2 动态环境下的路径规划与避障策略 5
3.3 多任务协同的运动控制优化方法 6
第4章 农业机械臂运动控制的实验验证与案例分析 7
4.1 实验平台搭建与测试方案设计 7
4.2 典型农业场景下的运动控制效果评估 7
4.3 案例分析 8
第5章 结论 9
参考文献 10
致 谢 11
