基于前馈隐马尔可夫模型的机器人演示轨迹精准重构方法研究  

Research on the precise reconstruction method of robot demonstration trajectory based on feedforward hidden Markov model

作　　者：苏永彬 洪瑞康 刘暾东 Su Yongbin;Hong Ruikang;Liu Tundong(Pen-Tung Sah Institute of Micro-Nano Science and Technology,Xiamen University,Xiamen 361005,China)

机构地区：[1]厦门大学萨本栋微米纳米科学技术研究院,厦门361005

出　　处：《仪器仪表学报》2023年第12期199-207,共9页Chinese Journal of Scientific Instrument

摘　　要：针对隐马尔可夫模型在机器人演示轨迹编码过程中存在的参数估计效率低,轨迹重构精度差的问题,提出了一种基于前馈隐马尔可夫模型的机器人演示学习方法。该方法通过林德-布佐-格雷(LBG)算法对采集的多条演示轨迹进行关键点识别,利用最小失真准则选取合适的轨迹计算模型初始化参数,结合鲍姆-韦尔奇算法完成模型参数估计。在此基础上,通过维特比算法计算每个样本点最可能的归属状态,并采用极大似然估计对归属每个状态的样本点重新计算状态参数,最后通过高斯混合回归获得重构轨迹。为验证算法的有效性,设计了手写字母轨迹数据集和晶圆机械臂自动上料轨迹的演示学习实验,并引入平均弗雷歇距离定量评估轨迹重构精度。实验结果表明:采用本文所提方法的重构轨迹在晶圆机械臂自动上料轨迹中的平均弗雷歇距离为5.49,较传统方法提升了15.15%的轨迹重构精度,具有良好的应用前景。A method for robot demonstration learning based on forward hidden Markov models is proposed to address the problems of low parameter estimation efficiency and poor trajectory reconstruction accuracy in the process of robot demonstration trajectory encoding.The method identifies key points of multiple collected demonstration trajectories using the Linde-Buzo-Gray(LBG)algorithm,selects appropriate trajectory calculation model initialization parameters using the minimum distortion criterion,and completes model parameter estimation by combining the Baum-Welch algorithm.On this basis,the Viterbi algorithm is used to calculate the most likely attribution state of each sample point,and the maximum likelihood estimation is utilized to recalculate the state parameters of each sample point attributed to each state.Finally,the reconstructed trajectory is obtained through Gaussian mixture regression.To evaluate the effectiveness of the algorithm,a handwritten letter trajectory dataset and a demonstration learning experiment of wafer mechanical arm automatic feeding trajectory are designed.The average Frchet distance is introduced to quantitatively evaluate the trajectory reconstruction accuracy.The experimental results show that the proposed method improves the trajectory reconstruction accuracy by 15.15%compared to traditional methods,with an average Frchet distance of 5.49 in the automatic wafer loading trajectory of the robotic arm,which indicates promising application prospects.

关 键 词：演示学习 LBG算法 隐马尔可夫模型 轨迹重构 

分 类 号：TH241.2[机械工程—机械制造及自动化]