Evaluating Accuracy of Vine Robot Shape Sensing with Distributed Inertial Measurement Units

TL;DR

This paper systematically evaluates the accuracy of distributed IMU sensors for shape sensing in vine robots, revealing key insights into drift, error rates, and optimal sensor spacing for active and passive steering.

Contribution

It provides the first comprehensive experimental analysis of distributed IMU-based shape sensing accuracy under various conditions for vine robots.

Findings

Average IMU orientation drift rate of 1.33 degrees/min

Tip position error of 11% for passive steering

Tip position error of 16% for active steering

Abstract

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.