用分布式陀螺仪测量藤蔓机器人的形状，验证了其在不同工况下的精度与局限。
Evaluating Accuracy of Vine Robot Shape Sensing with Distributed Inertial Measurement Units
- 通过多节点陀螺仪采集姿态数据，构建机器人全身形变估计模型。
- 主动转向时末端位置误差达16%，长度增长至175厘米时误差为8%。
- 中等传感器间距对单曲率形态误差最小，适合实际部署优化。
柔性延伸型藤蔓机器人适用于狭窄、布满障碍的环境，是城市搜救的理想选择。准确感知其全身形态有助于定位沿途传感器信息并确定机器人在探索空间中的构型。以往方法多依赖单一惯性测量单元（IMU）结合力传感或长度估计来定位末端，仅有一项研究在受控迷宫环境中使用分布式IMU实现了被动导向机器人的全身体态感知。然而，主动转向、不同机器人长度及传感器间距下，分布式IMU形变感知的精度尚未系统量化。本文实验评估了沿机器人本体分布的IMU在形变感知中的表现。测量得到15个传感器平均姿态漂移率为1.33度/分钟。被动转向时末端位置均方误差为机器人长度的11%；主动转向时上升至16%。在30-175厘米长度范围内的生长实验中，末端平均误差为8%，且随长度增加呈正向趋势。此外，分析传感器间距影响发现，中等间距可最小化单曲率形态下的误差。结果表明，分布式IMU可用于藤蔓机器人形变感知，同时揭示了建模与算法集成方面的关键限制与改进机会，为野外部署提供依据。
原文摘要 · Abstract (English)
Soft, tip-extending vine robots are well suited for navigating tight, debris-filled environments, making them ideal for urban search and rescue. Sensing the full shape of a vine robot's body is helpful both for localizing information from other sensors placed along the robot body and for determining the robot's configuration within the space being explored. Prior approaches have localized vine robot tips using a single inertial measurement unit (IMU) combined with force sensing or length estimation, while one method demonstrated full-body shape sensing using distributed IMUs on a passively steered robot in controlled maze environments. However, the accuracy of distributed IMU-based shape sensing under active steering, varying robot lengths, and different sensor spacings has not been systematically quantified. In this work, we experimentally evaluate the accuracy of vine robot shape sensing using distributed IMUs along the robot body. We quantify IMU drift, measuring an average orientation drift rate of 1.33 degrees/min across 15 sensors. For passive steering, mean tip position error was 11% of robot length. For active steering, mean tip position error increased to 16%. During growth experiments across lengths from 30-175 cm, mean tip error was 8%, with a positive trend with increasing length. We also analyze the influence of sensor spacing and observe that intermediate spacings can minimize error for single-curvature shapes. These results demonstrate the feasibility of distributed IMU-based shape sensing for vine robots while highlighting key limitations and opportunities for improved modeling and algorithmic integration for field deployment.
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