基于混合概率运动基元的机器人轨迹鲁棒性建模  

Robust Modeling of Robot Trajectories Based on Mixture Probabilistic Movement Primitives

作　　者：苏永彬 刘暾东 SU Yongbin;LIU Tundong(Pen-Tung Sah Institute of Micro-Nano Science and Technology,Xiamen University,Xiamen 361002)

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361102

出　　处：《机械工程学报》2025年第3期167-177,共11页Journal of Mechanical Engineering

摘　　要：针对大作业半径机器人在演示学习过程中存在的轨迹离群问题,提出一种基于混合概率运动基元的机器人演示轨迹鲁棒性建模方法。该方法采用基于动态时间规整的方法对采集到的演示轨迹进行预处理,通过映射矩阵路径寻优提升轨迹点的分布质量,解决了演示者动作卡顿与速度不均造成的轨迹时间长度不一问题。在此基础上,采用概率运动基元模型的置信区间设定轨迹点数量阈值,对轨迹样本进行聚类和权重分配;利用聚类结果和权重参数构建混合概率运动基元模型,抑制离群轨迹对模型参数的偏移,以提高模型鲁棒性。为验证方法有效性,设计了手写字母轨迹公共数据集和实际机械臂上料轨迹的演示学习实验,并引入平均弗雷歇距离和平均欧氏距离定量评估模型可靠性;实验结果表明:所提方法有效约束了离群轨迹对模型参数造成的偏移,与传统的概率运动基元模型和高斯混合模型相比表现出更强的鲁棒性,在大作业半径的工业机器人领域具有良好的应用前景。To address the issue of outlier trajectories in the demonstration learning process for robots with a wide operating range,a method for robust modeling of robot demonstration trajectories based on mixture probability motion primitives is proposed.The method first preprocesses the collected demonstration trajectories using dynamic time warping(DTW)to optimize the distribution of trajectory points by optimizing the mapping matrix.This resolves the problem of varying trajectory lengths caused by jerky demonstration movements and uneven speeds.After obtaining trajectory sets with equal time lengths,a trajectory point threshold based on confidence intervals in the probabilistic motion primitive model is used to cluster and assign weights to samples within the trajectory set.Finally,a mixture probability motion primitive model is constructed based on the clustering results and weight parameters to suppress the parameter deviation caused by outlier trajectories and improve model robustness.To validate the effectiveness of the proposed method,experiments were conducted using handwritten letter trajectory datasets and real robotic arm feeding trajectory demonstrations.Metrics such as average Fréchet distance and average Euclidean distance are introduced for quantitative evaluation of the model’s reliability.The experimental results demonstrate that the proposed method effectively mitigates the parameter deviation caused by outlier trajectories.It exhibits stronger robustness compared to traditional probabilistic motion primitive models and Gaussian mixture models,highlighting its promising application prospects in the field of industrial robots with large workspace radii.

关 键 词：演示学习 动态时间规整 概率运动基元 轨迹建模 

分 类 号：TH241[机械工程—机械制造及自动化]