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

• URL: http://arxiv.org/abs/2602.24202v1
• Date: Fri, 27 Feb 2026 17:28:42 GMT
• Title: Evaluating Accuracy of Vine Robot Shape Sensing with Distributed Inertial Measurement Units
• Abstract summary: We experimentally evaluate the accuracy of vine robot shape sensing using distributed IMUs along the robot body.<n>We also analyze the influence of sensor spacing and observe that intermediate spacings can minimize error for single-curvature shapes.
• Score: 1.1474575075646143
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• Decentralized Scalable Exploration via Emergent Adaptive Lévy Walks on Minimal-Sensing Platforms [0.0]
  We present a lightweight sensor-driven Lévy walk controller for aerial robots weighing under 50 grams.<n>The method combines discrete Lévy step-length sampling with a sensor-reactive heading policy using directional range measurements.<n> Simulation results show coverage improvements of 79.6% in open arenas, 43.1% in rooms-and-corridors layouts, and 13.6% in cluttered environments.
  arXiv  Detail & Related papers  (2026-07-28T01:53:28Z)
• Continuum Robot Localization using Distributed Time-of-Flight Sensors [14.753889983176194]
  We present a technique for continuum robots that relies on small, low-resolution ToF sensors distributed along the length of the robot.<n>We achieve an average localization error of 2.5cm in position and 7.2 in rotation across all experimental conditions with a 53cm long robot.
  arXiv  Detail & Related papers  (2026-02-06T21:42:37Z)
• Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation [34.47272224723296]
  We present Taccel, a high-performance simulation platform that integrates IPC and ABD to model robots, tactile sensors, and objects with both accuracy and unprecedented speed.<n>Unlike previous simulators that operate at sub-real-time speeds with limited parallelization, Taccel provides precise physics simulation and realistic tactile signals.<n>These capabilities position Taccel as a powerful tool for scaling up tactile robotics research and development, potentially transforming how robots interact with and understand their physical environment.
  arXiv  Detail & Related papers  (2025-04-17T12:57:11Z)
• A Soft e-Textile Sensor for Enhanced Deep Learning-based Shape Sensing of Soft Continuum Robots [0.3495246564946556]
  The safety and accuracy of robotic navigation hold paramount importance, especially in the realm of soft continuum robotics. Traditional rigid sensors often fail to integrate well with the flexible nature of these robots, adding unwanted bulk and rigidity. This study presents a new approach to shape sensing in soft continuum robots through the use of soft e-textile resistive sensors.
  arXiv  Detail & Related papers  (2024-04-19T05:00:25Z)
• Multi-Robot Relative Pose Estimation in SE(2) with Observability Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph Optimization [1.0485739694839669]
  We focus on cooperative localization and observability analysis of relative pose estimation. For ROS/Gazebo simulations, we explore four sensing and communication structures. In hardware experiments, two Turtlebot3 equipped with UWB modules are used for real-world inter-robot relative pose estimation.
  arXiv  Detail & Related papers  (2024-01-27T06:09:56Z)
• 6N-DoF Pose Tracking for Tensegrity Robots [5.398092221687385]
  Tensegrity robots are composed of rigid compressive elements (rods) and flexible tensile elements (e.g., cables) This work aims to address the pose tracking of tensegrity robots through a markerless, vision-based method. An iterative optimization process is proposed to estimate the 6-DoF poses of each rigid element of a tensegrity robot from an RGB-D video.
  arXiv  Detail & Related papers  (2022-05-29T20:55:29Z)
• Bayesian Imitation Learning for End-to-End Mobile Manipulation [80.47771322489422]
  Augmenting policies with additional sensor inputs, such as RGB + depth cameras, is a straightforward approach to improving robot perception capabilities. We show that using the Variational Information Bottleneck to regularize convolutional neural networks improves generalization to held-out domains. We demonstrate that our method is able to help close the sim-to-real gap and successfully fuse RGB and depth modalities.
  arXiv  Detail & Related papers  (2022-02-15T17:38:30Z)
• CNN-based Omnidirectional Object Detection for HermesBot Autonomous Delivery Robot with Preliminary Frame Classification [53.56290185900837]
  We propose an algorithm for optimizing a neural network for object detection using preliminary binary frame classification. An autonomous mobile robot with 6 rolling-shutter cameras on the perimeter providing a 360-degree field of view was used as the experimental setup.
  arXiv  Detail & Related papers  (2021-10-22T15:05:37Z)
• AuraSense: Robot Collision Avoidance by Full Surface Proximity Detection [3.9770080498150224]
  AuraSense is the first system to realize no-dead-spot proximity sensing for robot arms. It requires only a single pair of piezoelectric transducers, and can easily be applied to off-the-shelf robots.
  arXiv  Detail & Related papers  (2021-08-10T18:37:54Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• Relative Localization of Mobile Robots with Multiple Ultra-WideBand Ranging Measurements [15.209043435869189]
  We propose an approach to estimate the relative pose between a group of robots by equipping each robot with multiple UWB ranging nodes. To improve the localization accuracy, we propose to utilize the odometry constraints through a sliding window-based optimization.
  arXiv  Detail & Related papers  (2021-07-19T12:57:02Z)
• Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems [92.26462290867963]
  Kimera-Multi is the first multi-robot system that is robust and capable of identifying and rejecting incorrect inter and intra-robot loop closures. We demonstrate Kimera-Multi in photo-realistic simulations, SLAM benchmarking datasets, and challenging outdoor datasets collected using ground robots.
  arXiv  Detail & Related papers  (2021-06-28T03:56:40Z)
• Domain and Modality Gaps for LiDAR-based Person Detection on Mobile Robots [91.01747068273666]
  This paper studies existing LiDAR-based person detectors with a particular focus on mobile robot scenarios. Experiments revolve around the domain gap between driving and mobile robot scenarios, as well as the modality gap between 3D and 2D LiDAR sensors. Results provide practical insights into LiDAR-based person detection and facilitate informed decisions for relevant mobile robot designs and applications.
  arXiv  Detail & Related papers  (2021-06-21T16:35:49Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.