Adaptive optimization of teaching programming for robotic arms based on reinforcement learning
基于强化学习的机械臂示教编程自适应优化

摘要

机械臂示教编程在工业自动化领域广泛应用，但传统方式存在灵活性差、效率低等问题。为实现更高效智能的示教编程，强化学习技术被引入。通过构建包含状态空间、动作空间和奖励函数的强化学习模型，对机械臂示教编程过程进行自适应优化。模型以机械臂关节角度、末端执行器位姿等作为状态输入，以关节运动指令为动作输出，基于任务完成情况和能耗等设定奖励机制。经仿真与实验验证，该优化方法显著提升机械臂示教编程效率，降低操作复杂性，为机械臂在复杂任务场景下的应用提供有效技术支持。

Abstract

Teaching programming for robotic arms is widely used in industrial automation, but traditional methods suffer from issues such as poor flexibility and low efficiency. To achieve more efficient and intelligent teaching programming, reinforcement learning technology has been introduced. By constructing a reinforcement learning model that includes the state space, action space, and reward function, the teaching programming process for robotic arms can be optimized adaptively. The model uses the joint angles of the robotic arm and the end effector's position as state inputs, and the joint motion commands as action outputs. A reward mechanism is established based on the task completion and energy consumption. Simulation and experimental results show that this optimization method significantly enhances the efficiency of teaching programming for robotic arms, reduces operational complexity, and provides effective technical support for the application of robotic arms in complex task scenarios.