用数学方法实现软体机器人的实时整体形状控制
Shape control of simulated multi-segment continuum robots via Koopman operators with per-segment projection
- 基于柯普曼算子与分段投影，构建高效控制模型
- 模型精度提升一个数量级，支持复杂目标形状追踪
- 适合机器人控制、柔性系统研究者参考
软体连续型机器人可实现生物相容且柔性的运动，如章鱼触手的游动、爬行和操作。然而，当前最先进的连续型机器人仅能实现实时任务空间控制（即末端控制），无法实现整体形状控制，主要受限于其无穷自由度带来的高计算成本。本文提出一种基于数据驱动的柯普曼算子方法，用于模拟多段肌腱驱动的软体连续机器人（基于基尔霍夫杆模型）的形状控制。通过从仿真中收集数据，采用分段投影方案对机器人状态进行处理，使识别出的控制仿射柯普曼模型精度较无投影方案提升一个数量级。利用学习到的柯普曼模型，结合线性模型预测控制（MPC），可实现对多种复杂目标形状的闭环控制。该方法实现了计算高效的实时闭环控制，验证了软体机器人实时整体形状控制的可行性。我们相信此工作将为实际软体连续机器人的形状控制铺平道路。
原文摘要 · Abstract (English)
Soft continuum robots can allow for biocompatible yet compliant motions, such as the ability of octopus arms to swim, crawl, and manipulate objects. However, current state-of-the-art continuum robots can only achieve real-time task-space control (i.e., tip control) but not whole-shape control, mainly due to the high computational cost from its infinite degrees of freedom. In this paper, we present a data-driven Koopman operator-based approach for the shape control of simulated multi-segment tendon-driven soft continuum robots with the Kirchhoff rod model. Using data collected from these simulated soft robots, we conduct a per-segment projection scheme on the state of the robots allowing for the identification of control-affine Koopman models that are an order of magnitude more accurate than without the projection scheme. Using these learned Koopman models, we use a linear model predictive control (MPC) to control the robots to a collection of target shapes of varying complexity. Our method realizes computationally efficient closed-loop control, and demonstrates the feasibility of real-time shape control for soft robots. We envision this work can pave the way for practical shape control of soft continuum robots.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。