基于高斯过程的倾转旋翼机器人模型辨识  

Model Identification of Tiltrotors Robot Based on Gaussian Processes

作　　者：霍雨佳 李一平[1,2,3,4] 封锡盛 HUO Yujia;LI Yiping;FENG Xisheng(State Key Laboratory of Robotics,Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang Liaoning 110016,China;Institutes for Robotics and Intelligent Manufacturing,Chinese Academy of Sciences,Shenyang Liaoning 110169,China;Key Laboratory of Marine Robotics,Liaoning Province,Shenyang Liaoning 110169,China;University of Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]中国科学院沈阳自动化研究所机器人学国家重点实验室,辽宁沈阳110016 [2]中国科学院机器人与智能制造创新研究院,辽宁沈阳110169 [3]辽宁省水下机器人重点实验室,辽宁沈阳110169 [4]中国科学院大学,北京100049

出　　处：《机床与液压》2022年第11期8-13,共6页Machine Tool & Hydraulics

基　　金：国家自然科学基金重大研究计划(91648204)。

摘　　要：针对一种新型倾转旋翼水空跨域机器人,采用一种非参数系统辨识算法,目的是在数据量较少情况下,实现对跨域机器人的动力学建模。该算法基于多输出高斯过程,在无需机器人动力学模型的先验知识情况下,可通过采集试验数据,获得机器人的模型。为降低高斯过程回归的计算复杂性,采用局部高斯过程回归,对机器人的动力学模型进行辨识。为验证该辨识方法的有效性,在空气中以倾转四旋翼模型为试验平台,利用遥控获取的机器人数据进行验证。在机器人相同控制输入量下,对比模型预测值和实际测量值,证明该机器人动力学辨识方法的有效性和局部高斯过程回归的快速性。For a new tiltrotor cross-domain robot,a non-parametric system identification algorithm was used to realize the dynamic modeling for the cross-domain robot with less data.By using the algorithm,based on multioutput Gaussian processes and in the absence of prior knowledge of the robot dynamic model,the robot model could be obtained by collecting experimental data.In order to reduce the computational complexity of the Gaussian process regression,local Gaussian process regression was used to identify the dynamic model of the robot.To verify the effectiveness of the identification method,the quad-tiltrotors rotor model was used as the test platform with the robot data obtained through remote control.Under the same control input of the robot,the effectiveness of the robot dynamics identification method and the rapidity of the local Gaussian process regression were proved.

关 键 词：倾转旋翼机器人 高斯过程 动力学辨识 

分 类 号：TP242.2[自动化与计算机技术—检测技术与自动化装置]