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Updated: Sep 16, 2025

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Technology and Method Optimization for Foot-Ground Contact Force Detection in Wheel-Legged Robots.

Chao Huang1, Meng Hong1, Yaodong Wang1

  • 1Hubei Provincial Engineering Research Center of Robotics & Intelligent Manufacturing, Wuhan University of Technology, Wuhan 430070, China.

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Summary

This study introduces an advanced method for wheel-legged robots to accurately detect foot-ground contact forces. This improves robot stability and navigation in challenging terrains.

Keywords:
Gaussian process regressionartificial neural networkcontact force estimationneural networkpiezoelectric film sensorsquadruped robot

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Area of Science:

  • Robotics
  • Mechanical Engineering
  • Sensor Technology

Background:

  • Wheel-legged robots offer enhanced terrain adaptability by merging wheeled and legged locomotion.
  • Accurate perception of foot-ground contact forces is crucial for these robots but challenging, especially with flexible tire-ground interactions.
  • Existing methods struggle with precise estimation of contact positions and 3D forces.

Purpose of the Study:

  • To develop a robust foot-ground contact state detection and optimization technique for wheel-legged robots.
  • To improve the accuracy of 3D contact force estimation in dynamic and flexible contact scenarios.
  • To enhance stable contact perception and motion decision-making for robots operating in complex environments.

Main Methods:

  • Utilized finite element analysis (FEA) to simulate strain distribution and global sensitivity analysis (GSA) for optimal PVDF sensor placement.
  • Developed a custom experimental platform to collect dynamic contact data under variable gait conditions.
  • Employed Gaussian process regression (GPR) and artificial neural network (ANN) models for predicting 3D contact forces from sensor data.

Main Results:

  • Optimized PVDF sensor placement was experimentally validated.
  • GPR and ANN models accurately predicted dynamic 3D contact forces, achieving a normalized root mean square error (NRMSE) as low as 8.04%.
  • The models demonstrated reliable repeatability and generalization capabilities for novel inputs.

Conclusions:

  • The proposed multi-sensor fusion and intelligent modeling approach significantly enhances the accuracy of foot-ground contact force estimation in wheel-legged robots.
  • This technique provides a reliable foundation for stable contact perception and adaptive motion control in complex terrains.
  • The findings offer valuable technical support for advancing the capabilities of autonomous wheel-legged robots.