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Vision-based dynamic modeling of wheeled-legged robot considering slippage using Gibbs–Appell formulation

Published online by Cambridge University Press:  07 November 2024

M. H. Korayem Open the ORCID record for M. H. Korayem [Opens in a new window] ,
N. Nikseresht  and
A. H. Asadi
M. H. Korayem*
Affiliation:
Robotics Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
N. Nikseresht
Affiliation:
Robotics Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
A. H. Asadi
Affiliation:
Robotics Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
*
Corresponding author: M. H. Korayem; Email: [email protected]
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Abstract

This study focuses on the kinematic and dynamic modeling of a wheeled-legged robot (WLR), taking into account kinematic and dynamic slippage. In this regard, the Gibbs–Appell formulation was utilized to derive dynamic equations. Determining the slippage in the wheels for movement equations is a challenging task due to its dependency on factors such as the robot’s postures, velocities, and surface characteristics. To address this challenge, machine vision was used to quantify the slippage of the wheels on the body based on the pose estimation method. This data served as input for movement equations to analyze the robot’s deviation from its path and posture. In the following, the robot’s movement was simulated using Webots and MATLAB, followed by various experimental tests involving acceleration and changes in leg angles on the WLR. The results were then compared to the simulations to demonstrate the accuracy of the developed system modeling. Additionally, an IMU sensor was utilized to measure the robot’s motion and validate against machine vision data. The findings revealed that neglecting the slippage of the wheels in the robot’s motion modeling resulted in errors ranging from 5% to 11.5%. Furthermore, lateral slippage ranging from 1.1 to 5.2 cm was observed in the robot’s accelerated movement. This highlights the importance of including lateral slippage in the equations for a more precise modeling of the robot’s behavior.

Keywords

Wheeled-legged robotGibbs–Appellpose estimationwheel slippagedynamic modelingmachine visionquasi coordinate
Type
Research Article
Information
Robotica , Volume 42 , Issue 11 , November 2024 , pp. 3784 - 3802
Copyright
© The Author(s), 2024. Published by Cambridge University Press

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