宇航学报 ›› 2022, Vol. 43 ›› Issue (7): 974-982.doi: 10.3873/j.issn.1000-1328.2022.07.014

• 环境试验与器件 • 上一篇    

机器人动态受力感知及零重力运动模拟技术

胡瑞钦,孟少华,张成立,董悫,张立建   

  1. 1. 北京卫星环境工程研究所,北京 100094;2. 北京市航天产品智能装配技术与装备工程技术研究中心,北京 100094
  • 收稿日期:2021-10-16 修回日期:2021-12-11 出版日期:2022-07-15 发布日期:2022-07-15
  • 基金资助:
    国防基础科研计划(JCKY2021203B037);航天科技集团公司五院杰出青年人才基金(R WY JQRC 15)

Robot Dynamic Force Perception and Zero gravity Motion Simulation Technology

HU Ruiqin, MENG Shaohua, ZHANG Chengli, DONG Que, ZHANG Lijian   

  1. 1. Beijing Institute of Spacecraft Environment Engineering, Beijing 100094, China;2. Beijing Engineering Research Center of Intelligent Assembly Technology and Equipment for Aerospace Product, Beijing 100094, China
  • Received:2021-10-16 Revised:2021-12-11 Online:2022-07-15 Published:2022-07-15

摘要: 针对航天器在轨服务任务的地面零重力模拟需求,研究基于工业机器人的零重力运动模拟技术。建立基于BP神经网络的受力感知预测模型,该模型采用机器人末端姿态、加速度、角速度和角加速度作为输入层参数,采用机器人末端六维力传感器数据作为输出层参数,实现了对机器人末端负载的高精度动态受力感知。设计正交试验方法确定机器人的运动路径点进行样本数据采集,实现了受力感知预测模型对机器人全工作空间的覆盖。进一步,基于对机器人末端负载的受力感知数据,应用动力学理论计算负载在失重状态下的运动速度,并控制机器人执行相应的运动,实现了对机器人末端负载的零重力运动模拟。

关键词: 在轨服务, 零重力模拟, BP神经网络, 机器人控制

Abstract: Aiming at the requirements of ground zero gravity simulation for spacecraft on orbit servicing missions, the zero gravity motion simulation technology based on industrial robot is studied. A force perception prediction model based on BP neural network is established. The model uses the attitude, acceleration, angular velocity and angular acceleration of the robot end as the input layer parameters, and the six dimensional force sensor data of the robot end as the output layer parameters. The high precision dynamic force perception of the robot end load is realized. The orthogonal experiment method is designed to determine the motion path points of the robot for sample data acquisition, and the force perception prediction model is realized to cover the whole workspace of the robot. Furthermore, based on the force perception data of the end load of the robot, the dynamics theory is applied to calculate the motion speed of the load in the state of weightlessness, and the robot is controlled to perform the corresponding motion, and the zero gravity motion simulation of the end load of the robot is realized.

Key words: On orbit servicing, Zero gravity simulation, BP neural network, Robot control

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