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

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.

Figures (9)

• Figure 1: Summary of contributions. We quantified the shape sensing accuracy of our vine robot instrumented with inertial measurement unit (IMU) sensors along its length at various (a) passive and (b) active steering angles, (c) robot lengths, and (d) sensor spacings. We also measured the effect on individual IMU orientation accuracy of drift over time.
• Figure 2: Vine robot shape sensing system. The vine robot includes a main chamber, three series pouch motor (SPM) steering actuators, and a base for growth and retraction. Eighteen IMUs are distributed along the robot body and connected through multiplexers to a microcontroller at the base.
• Figure 3: Orientation offsetting procedure. Yellow reference frames represent the orientation of each IMU$_i$, and red reference frames represent the orientation of IMU$_{i-1}$, the previous IMU (closer to the base). (a) Raw IMU orientations when the robot is straight. (b) After offsetting, consecutive IMU orientations are aligned by subtracting the stored relative orientation measured in the straight configuration.
• Figure 4: Vine robot model parameters used for shape sensing algorithm. Consecutive IMUs, indexed $i$ and $i+1$, define a local segment of length $s$ with inflated diameter $d$. An orientation difference $\theta$ between IMUs is modeled as a single bend located midway between them, consisting of a circular arc of length $L_{arc}$ and two straight segments of length $L_{straight}$.
• Figure 5: IMU drift test results. Orientation error relative to the initial offset shown as a function of time since offsetting. Each color corresponds to one of 15 IMUs, with one representative trial per unit. The black line indicates a linear fit across all data ($1.33^{\circ}$/min, $R^2$ = 0.16, $p$=0.0024).