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VTire: A Bimodal Visuotactile Tire with High-Resolution Sensing Capability

Shoujie Li, Jianle Xu, Tong Wu, Yang Yang, Yanbo Chen, Xueqian Wang, Wenbo Ding, Xiao-Ping Zhang

TL;DR

VTire presents a bimodal visuotactile tire capable of simultaneous high-resolution tactile and visual sensing to enhance terrain understanding and tire health assessment. It couples an innovative manufacturing/material design with a transformer-based multimodal classification framework, FEA-based load sensing, and FCN segmentation, validated on a mobile platform with open-source datasets and code. The approach yields state-of-the-art performance on terrain classification (≈99%), object search (≈98%), crack/damage detection (≈98–97%), and load sensing (≈0.75 kg precision), demonstrating practical applicability for robotic and vehicle systems in complex environments. While effective, the single-camera configuration limits high-speed operation, motivating future work toward faster vision systems, broader deployment, and tighter integration with autonomous platforms.

Abstract

Developing smart tires with high sensing capability is significant for improving the moving stability and environmental adaptability of wheeled robots and vehicles. However, due to the classical manufacturing design, it is always challenging for tires to infer external information precisely. To this end, this paper introduces a bimodal sensing tire, which can simultaneously capture tactile and visual data. By leveraging the emerging visuotactile techniques, the proposed smart tire can realize various functions, including terrain recognition, ground crack detection, load sensing, and tire damage detection. Besides, we optimize the material and structure of the tire to ensure its outstanding elasticity, toughness, hardness, and transparency. In terms of algorithms, a transformer-based multimodal classification algorithm, a load detection method based on finite element analysis, and a contact segmentation algorithm have been developed. Furthermore, we construct an intelligent mobile platform to validate the system's effectiveness and develop visual and tactile datasets in complex terrains. The experimental results show that our multimodal terrain sensing algorithm can achieve a classification accuracy of 99.2\%, a tire damage detection accuracy of 97\%, a 98\% success rate in object search, and the ability to withstand tire loading weights exceeding 35 kg. In addition, we open-source our algorithms, hardware, and datasets at https://sites.google.com/view/vtire.

VTire: A Bimodal Visuotactile Tire with High-Resolution Sensing Capability

TL;DR

VTire presents a bimodal visuotactile tire capable of simultaneous high-resolution tactile and visual sensing to enhance terrain understanding and tire health assessment. It couples an innovative manufacturing/material design with a transformer-based multimodal classification framework, FEA-based load sensing, and FCN segmentation, validated on a mobile platform with open-source datasets and code. The approach yields state-of-the-art performance on terrain classification (≈99%), object search (≈98%), crack/damage detection (≈98–97%), and load sensing (≈0.75 kg precision), demonstrating practical applicability for robotic and vehicle systems in complex environments. While effective, the single-camera configuration limits high-speed operation, motivating future work toward faster vision systems, broader deployment, and tighter integration with autonomous platforms.

Abstract

Developing smart tires with high sensing capability is significant for improving the moving stability and environmental adaptability of wheeled robots and vehicles. However, due to the classical manufacturing design, it is always challenging for tires to infer external information precisely. To this end, this paper introduces a bimodal sensing tire, which can simultaneously capture tactile and visual data. By leveraging the emerging visuotactile techniques, the proposed smart tire can realize various functions, including terrain recognition, ground crack detection, load sensing, and tire damage detection. Besides, we optimize the material and structure of the tire to ensure its outstanding elasticity, toughness, hardness, and transparency. In terms of algorithms, a transformer-based multimodal classification algorithm, a load detection method based on finite element analysis, and a contact segmentation algorithm have been developed. Furthermore, we construct an intelligent mobile platform to validate the system's effectiveness and develop visual and tactile datasets in complex terrains. The experimental results show that our multimodal terrain sensing algorithm can achieve a classification accuracy of 99.2\%, a tire damage detection accuracy of 97\%, a 98\% success rate in object search, and the ability to withstand tire loading weights exceeding 35 kg. In addition, we open-source our algorithms, hardware, and datasets at https://sites.google.com/view/vtire.
Paper Structure (22 sections, 6 equations, 17 figures, 3 tables)

This paper contains 22 sections, 6 equations, 17 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Hardware Design
  4. System Structure
  5. Proposed Smart Tire
  6. Sensing skin
  7. Imaging system
  8. Lighting system
  9. Algorithm Design
  10. Multimodal Terrain Classification Algorithm
  11. Load Weight Sensing Algorithm
  12. Floor Crack and Contact Object Segmentation Algorithm
  13. Experiments
  14. Bimodal Terrain Classification (VTire Bimodal Data)
  15. Multimodal Terrain Classification (VTire Bimodal Data + External Visual Data)
  16. ...and 7 more sections

Figures (17)

  • Figure 1: Introduction to the bimodal Tire. Functions that can be achieved with tires: (a) Terrain classification in complex scenes; (b) Ground cracks and objects searching; (c) Tire damage detection; (d) Weight-loaded detection.
  • Figure 2: Hardware structure diagram. (a) Side view of the motion platform; (b) The mobile platform exploded view; (c) VTire exploded view.
  • Figure 3: The mobile platform framework diagram. The mobile platform consists of a power system, a sensing system, a control system, and a motion system.
  • Figure 4: Fabrication processes of (a) the wheel hub; (b) the transparent layer; (c) the reflective layer.
  • Figure 5: Comparison of different solutions. (a) Multi-camera solution; (b) Single camera solution; (c) Top lighting solution; (d) Side lighting solution. (e) Section view. (f) Connection of components.
  • ...and 12 more figures