用内置传感器实时估算张拉整体结构形状，无需外部设备。
Real-Time Shape Estimation of Tensegrity Structures Using Strut Inclination Angles
- 基于能量最小化原理，仅用惯性传感器估计结构形态。
- 实验验证在外界干扰下仍能实现高精度实时形状重建。
- 适合软体机械臂等无明确关节结构的机器人系统使用。
张拉整体结构在机器人领域应用日益广泛，如可连续弯曲的软体机械臂和移动机器人，用于动态探索未知不平环境。其形状估计作为状态基础，对控制至关重要。然而，由于缺乏明确的关节结构，传统角度传感器（如电位计或编码器）难以适用，基于机载传感器的形状估计依然面临挑战。目前尚无研究成功实现仅依赖机载传感器（如惯性测量单元IMUs）的形状估计。本文提出一种新方法，通过能量最小化实现形状估计，并在简单的一类张拉整体结构上进行了实验验证。结果表明，该算法能利用机载传感器在存在外部扰动的情况下实时准确估计结构形状。
原文摘要 · Abstract (English)
Tensegrity structures are becoming widely used in robotics, such as continuously bending soft manipulators and mobile robots to explore unknown and uneven environments dynamically. Estimating their shape, which is the foundation of their state, is essential for establishing control. However, on-board sensor-based shape estimation remains difficult despite its importance, because tensegrity structures lack well-defined joints, which makes it challenging to use conventional angle sensors such as potentiometers or encoders for shape estimation. To our knowledge, no existing work has successfully achieved shape estimation using only onboard sensors such as Inertial Measurement Units (IMUs). This study addresses this issue by proposing a novel approach that uses energy minimization to estimate the shape. We validated our method through experiments on a simple Class 1 tensegrity structure, and the results show that the proposed algorithm can estimate the real-time shape of the structure using onboard sensors, even in the presence of external disturbances.
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