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.

翻译：柔软、尖端可延伸的藤蔓机器人非常适合在狭窄且充满碎屑的环境中导航，使其成为城市搜救任务的理想选择。感知藤蔓机器人全身形状既有助于定位沿机器人本体布置的其他传感器信息，也能确定机器人在探索空间内的构型。现有方法多通过单个惯性测量单元结合力传感或长度估计来实现藤蔓机器人尖端定位，而一种方法曾在受控迷宫环境中利用分布式IMU在被动转向机器人上实现了全身形状感知。然而，基于分布式IMU的形状感知在主动转向、可变机器人长度及不同传感器间距条件下的精度尚未得到系统量化。本研究通过实验评估了沿机器人本体布置分布式IMU进行藤蔓机器人形状感知的精度。我们量化了IMU漂移，测得15个传感器的平均姿态漂移率为1.33度/分钟。在被动转向条件下，尖端位置平均误差为机器人长度的11%；主动转向时该误差增至16%。在30-175厘米长度范围内的生长实验中，尖端平均误差为8%，且随长度增加呈上升趋势。我们还分析了传感器间距的影响，发现中等间距能最小化单曲率形状的误差。这些结果证明了基于分布式IMU的形状感知在藤蔓机器人上应用的可行性，同时揭示了其关键局限性，并为改进建模算法以适配实际部署提供了优化方向。