空间软体机械臂的两阶段神经网络控制方法

Two-stage neural network control method for space soft manipulato

中文关键词  在轨服务  软体机械臂建模和控制  两阶段神经网络  Transformer

英文关键词  on-orbit service  modeling and control of soft robotic arms  two-stage neural network  Transformer

中文摘要

      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.

英文摘要

      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.