Two-stage neural network control method for space soft manipulator
CUI Chao-chen
ZHANG Xiang
XIONG Dan
HAN Wei
HUANG Yi-yong
Abstract：Soft robotic arms,with their characteristics of lightweight,low cost,and flexible operation,hold tremendous potential for on-orbit servicing tasks.However,the inverse kinematics modeling and control of soft robotic arms remain challenging.As an alternative solution,the application of data-driven methods to learn numerical models of soft robotic arms has shown some success.Building upon previous research,this paper proposes an end-to-end two-stage neural network control approach and an asynchronous Transformer execution strategy for soft robotic arms.Comparative analysis with single-stage neural networks,traditional backpropagation(BP),long short-term memory(LSTM),and other two-stage methods from prior studies demonstrates that the approach presented in this paper achieves higher control precision.Finally,practical grasping experiments with a physical soft robotic arm validate the feasibility of the proposed method.
Keywords：on-orbit servicemodeling and control of soft robotic armstwo-stage neural networkTransformer
Publication Date：2023-12-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 2257-2264 )