用几何约束与传感器融合，实现绳索机器人高精度自感知
Tensegrity Robot Proprioceptive State Estimation with Geometric Constraints
- 结合三杆棱柱几何约束与IMU/编码器数据重建机器人形态
- 实测平均漂移仅4.2%，接近传统刚体机器人性能
- 适合需在复杂环境自主运行的柔性机器人研究者
张拉整体机器人由刚性杆与弹性缆索协同构成，具备抗冲击能力，但其运动学与动力学特性复杂，导致控制与状态估计困难。本文提出一种新型本体感知状态估计算法：首先利用三杆棱柱结构的几何约束，结合惯性测量单元（IMU）与电机编码器数据，重构机器人的形状与姿态；随后采用基于接触的不变扩展卡尔曼滤波器，结合正向运动学模型，估计机器人的全局位置与姿态。在仿真与真实张拉整体机器人平台上进行验证，该算法平均漂移率为4.2%，与传统刚体机器人状态估计性能相当。该方法可基于机载传感器实时运行，为张拉整体机器人在非结构化环境中的全自主运行奠定基础。
原文摘要 · Abstract (English)
Tensegrity robots, characterized by a synergistic assembly of rigid rods and elastic cables, form robust structures that are resistant to impacts. However, this design introduces complexities in kinematics and dynamics, complicating control and state estimation. This work presents a novel proprioceptive state estimator for tensegrity robots. The estimator initially uses the geometric constraints of 3-bar prism tensegrity structures, combined with IMU and motor encoder measurements, to reconstruct the robot's shape and orientation. It then employs a contact-aided invariant extended Kalman filter with forward kinematics to estimate the global position and orientation of the tensegrity robot. The state estimator's accuracy is assessed against ground truth data in both simulated environments and real-world tensegrity robot applications. It achieves an average drift percentage of 4.2%, comparable to the state estimation performance of traditional rigid robots. This state estimator advances the state of the art in tensegrity robot state estimation and has the potential to run in real-time using onboard sensors, paving the way for full autonomy of tensegrity robots in unstructured environments.
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