摘要
软体机械臂凭借质量轻、价格低、操作灵活等特性在在轨服务任务中具有巨大应用前景.然而,针对软体机械臂的逆运动学建模和控制仍是一个具有挑战性的问题.作为一种替代方案,采用数据驱动的方法对软体机械臂数值模型进行学习取得了一定成果.本文在前人研究的基础上,提出一种端到端的两阶段神经网络软体机械臂控制思想和异步Transformer执行策略.文章通过与单阶段神经网络、传统的BP、LSTM等构建的两阶段方法进行对比,结果表明:本文方法具有更高的控制精度.最后,利用软体机械臂实物进行抓取实验,验证了本文方法的可行性.
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.
作者
崔朝臣
张翔
熊丹
韩伟
黄奕勇
CUI Chao-chen;ZHANG Xiang;XIONG Dan;HAN Wei;HUANG Yi-yong(National Innovation Institute of Defense Technology,Academy of Military Science,Beijing 100071,China)
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2023年第12期2257-2264,共8页
Control Theory & Applications