深度Q-RBF网络下的瓶装食品装箱机械臂无碰轨迹规划

Collision-Free Trajectory Planning for Manipulator Based on Deep Q-RBF Reinforcement Learning Network

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摘要由于机械臂存在高度的灵活性,可模拟人类手臂完成易碎瓶装食品的装箱工作,并实时矫正机械臂轨迹规划所存在的误差,提升稳定性与精准度。分析装箱机械臂的基本架构,提出基于深度Q-RBF强化学习网络的机械臂无碰轨迹规划模型,通过资源分配自适应方法,根据待建模的样本,调整RBF网络隐含层单元,从而提升网络学习速率与在线学习能力,结合自适应Q强化学习算法,获得最优操作集合。并选用学习率调参法完成网络的参数学习。仿真与实验结果表明:与其他两种方法对比,此方法具有较强的避障能力,机械臂能够较好地依据预定轨迹行进;避碰进程变化缓和,且能够尽快收敛并逐步趋向稳定。 Due to the high flexibility of the robot arm,it can simulate human arm to complete packing work of fragile bottled food,and correct errors existing in trajectory planning in real time to improve the stability and accuracy.Based on the basic architecture of the boxed manipulator,a non-collision trajectory planning model for the manipulator was proposed based on deep Q-RBF reinforcement learning network.The hidden layer units of the RBF network were adjusted according to the samples to be modeled through the resource allocation adaptive method,so as to improve the network learning rate and online learning ability.The optimal operation set was obtained by combining the adaptive Q-reinforcement learning algorithm.The learning rate adjustment method was used to complete the parameter learning of the network.The simulation and experimental results show that,compared with the other two methods,the method has strong obstacle avoidance ability,and the manipulator can move according to the predetermined trajectory better;the change of collision avoidance process is moderate,and can converge as soon as possible and gradually tend to be stable.

作者任银广王平毛金凤 REN Yinguang;WANG Ping;MAO Jinfeng(Guang’an Vocational and Technical College,Guang’an Sichuan 638000,China;School of Electrical Engineering and Information Engineering,Lanzhou University of Technology,Lanzhou Gansu 735000,China;Key Laboratory of Robotics in Special Environment of Machinery Industry,Lanzhou University of Technology,Lanzhou Gansu 735000,China)

机构地区广安职业技术学院兰州理工大学电气工程与信息工程学院兰州理工大学机械工业特殊环境机器人重点实验室

出处《机床与液压》北大核心 2023年第5期89-95,共7页 Machine Tool & Hydraulics

基金国家自然科学基金青年科学基金项目(62001198) 甘肃省青年科技基金计划(20JR10RA186 21JR7RA247)。

关键词机械臂轨迹规划稳定性精准度 RBF网络 Manipulator Trajectory planning Stability Precision RBF network

分类号 TH115 [机械工程—机械设计及理论]

作者简介任银广(1983-),男,学士,讲师,研究方向为机械臂、机械设计制造及其自动化。E-mail:3558442192@qq.com。