用机械自身动态做姿态估计，让轻量蛇形机械臂更准更省
Pose Estimation of a Cable-Driven Serpentine Manipulator Utilizing Intrinsic Dynamics via Physical Reservoir Computing
- 利用机械臂固有非线性动力学作为物理储层进行姿态估计
- 实验误差仅4.3毫米，远低于解析法的39.5毫米
- 适合追求轻量化与高精度控制的柔性机械臂研究者
缆索驱动的蛇形机械臂在非结构化环境中具有巨大潜力，具备避障、多向受力和轻量化设计优势。通过将所有电机和传感器置于基座并使用柔性连杆，可进一步减轻臂体重量。为验证该理念，我们构建了一台9自由度的缆索驱动蛇形机械臂，臂长545毫米，总质量仅308克。然而，该设计引入了由缆索松弛、伸长及连杆变形引起的柔性变化，导致解析预测与实际关节位置存在偏差，使姿态估计更具挑战性。为此，我们提出一种基于物理储层计算的姿态估计方法，利用机械臂内在的非线性动力学作为高维储层。实验结果表明，本方法均方姿态误差为4.3毫米，优于基准长短期记忆网络的4.4毫米，显著优于解析方法的39.5毫米。该工作为轻量化缆索驱动蛇形机械臂的控制与感知策略提供了新方向。
原文摘要 · Abstract (English)
Cable-driven serpentine manipulators hold great potential in unstructured environments, offering obstacle avoidance, multi-directional force application, and a lightweight design. By placing all motors and sensors at the base and employing plastic links, we can further reduce the arm's weight. To demonstrate this concept, we developed a 9-degree-of-freedom cable-driven serpentine manipulator with an arm length of 545 mm and a total mass of only 308 g. However, this design introduces flexibility-induced variations, such as cable slack, elongation, and link deformation. These variations result in discrepancies between analytical predictions and actual link positions, making pose estimation more challenging. To address this challenge, we propose a physical reservoir computing based pose estimation method that exploits the manipulator's intrinsic nonlinear dynamics as a high-dimensional reservoir. Experimental results show a mean pose error of 4.3 mm using our method, compared to 4.4 mm with a baseline long short-term memory network and 39.5 mm with an analytical approach. This work provides a new direction for control and perception strategies in lightweight cable-driven serpentine manipulators leveraging their intrinsic dynamics.
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