基于含全局与局部可观测量的Koopman算子的多段柔性机械臂实时形状控制
Real-Time Shape Control of Multi-Segment Soft Robotic Arms Using Koopman Operators with Global and Local Observables
浏览论文内容
中文总结 AI 辅助
本文针对多段柔性机械臂形状控制难题，提出结合全局与局部可观测量的Koopman算子模型预测控制框架，经数值与物理实验验证，该框架可实现多段柔性机械臂的实时、可扩展且精准的形状控制。
中文摘要 AI 辅助
多段柔性机械臂可连续重构自身形状以实现安全交互，但仅控制末端执行器不足以完成受限空间任务。因此，形状控制对多段柔性机械臂而言是比末端控制更重要的任务，但由于连续体变形的高维非线性动力学特性，该任务仍具挑战性。现有研究中，形状控制精度由全局坐标系下的误差（全局形状误差）定义。对于多段柔性机械臂，仅将全局形状误差作为控制目标是不够的，因为段间耦合、重力诱导载荷及惯性效应会变得更为显著，且该难度随段数增加而增大。本文提出一种结合全局与局部可观测量的基于Koopman算子的模型预测控制框架，可实现多段柔性机械臂的实时形状控制。该框架通过数值与物理实验进行评估：数值实验验证了所提控制器的可扩展性，其可对最多含10个独立驱动段的机器人实现形状控制；物理实验表明，该控制器具备以下能力：（1）对3段和5段机械臂实现末端速度最高达0.6m/s的实时形状控制；（2）无需重新训练即可实现鲁棒跟踪，包括承受末端负载最高达400g及从7N横向扰动中恢复；（3）通过受限空间演示展现出未来用于检测应用的潜力。这些结果表明，所提框架可实现多段柔性机械臂的动态、可扩展且精准的实时形状控制。
英文摘要
Multi-segment soft robotic arms can continuously reconfigure their body shapes for safe interaction, but tip control alone is insufficient for constrained-space tasks. Therefore, shape control is a more important task for multi-segment soft arms than tip control, but remains challenging due to the high dimensionality and nonlinear dynamics of continuum deformation. In existing work, shape control accuracy is defined by the error in the global frame (global shape error). For multi-segment soft arms, using only global shape error as the control objective is insufficient, as segment coupling, gravity-induced loading, and inertial effects become more significant. This difficulty increases with the number of segments. In this paper, we present a Koopman-based model predictive control framework that combines global and local observables, enabling real-time shape control on multi-segment soft robotic arms. The framework is evaluated through numerical and physical experiments. Numerical experiments demonstrate the scalability of the proposed controller by achieving shape control on robots with up to 10 independently actuated segments. The physical experiments demonstrate that the controller is capable of (1) real-time shape control of 3- and 5-segment robotic arms with tip speeds up to 0.6 m/s, (2) robust tracking without retraining, including distal payloads up to 400~g and recovery from a 7~N lateral disturbance, and (3) the potential for future inspection applications through a confined-space demonstration. These results demonstrate that the proposed framework enables dynamic, scalable, and accurate real-time shape control on multi-segment soft robotic arms.
