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一种新型刚柔混合软体机器人运动学预测

CHEN Gen XU Linsen WANG Zhihuan ZHANG Hengwei LIU Zhipeng CAI Jianchen

机械科学与技术2025，Vol.44Issue(12)：2106-2115,10.
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机械科学与技术2025，Vol.44Issue(12)：2106-2115,10.DOI:10.13433/j.cnki.1003-8728.20230379

一种新型刚柔混合软体机器人运动学预测

Kinematic Prediction of Novel Soft-rigid Hybrid Soft Robotics

CHEN Gen 1XU Linsen 2WANG Zhihuan 1ZHANG Hengwei 1LIU Zhipeng 3CAI Jianchen1

作者信息

  • 1. College of Mechanical and Electrical Engineering,Hohai University,Changzhou 213000,Jiangsu,China
  • 2. College of Mechanical and Electrical Engineering,Hohai University,Changzhou 213000,Jiangsu,China||Changzhou Key Laboratory of Intelligent Manufacturing Technology and Assembly,Changzhou 213022,Jiangsu,China||Suzhou Research Institute,Hohai University,Suzhou 215004,Jiangsu,China
  • 3. Hefei Institutes of Physical Science,Chinese Academy of Sciences,Hefei 230031,China
  • 折叠

摘要

Abstract

Due to the non-linearity and variability of materials,the complexity of calculation and the instability of load,the establishment of kinematics of soft actuators is still a challenging task.A machine learning approach is proposed to obtain the kinematic mapping of a soft robot in 3D space to solve the problem of the difficulty of the traditional modeling approach.A finite element model(FEM)is also used to generate a large amount of training data to ensure the reliability of the trained model.The optimal solution is obtained by comparing the optimized BP neural network and the polynomial regression model.The results show that the average position error is within 1 mm and the average pressure error is within 1.6 kPa.The machine learning approach for solving the kinematics of soft actuators is simpler and more accurate than the usual analysis methods.

关键词

软体执行器/刚柔混合/神经网络/机器学习/软体机器人

Key words

soft actuators/soft-rigid hybrid/neural networks/machine learning/soft robotics

分类

矿业与冶金

引用本文复制引用

CHEN Gen,XU Linsen,WANG Zhihuan,ZHANG Hengwei,LIU Zhipeng,CAI Jianchen..一种新型刚柔混合软体机器人运动学预测[J].机械科学与技术,2025,44(12):2106-2115,10.

基金项目

常州市科技支撑计划(CE20225037) （CE20225037）

机械科学与技术

OA北大核心

1003-8728

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