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Comparison of Two System Identification Approaches for a Four-Wheel Differential Robot Based on Velocity Command

Diego Guffanti¹, Moisés Filiberto Mora Murillo^2,3, Marco Alejandro Hinojosa⁴

¹Universidad UTE, Av. Mariscal Sucre, Quito 170129, Ecuador.

Sensors (Basel, Switzerland)

|September 19, 2025

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The Motor-Based Model (MBM) offers superior accuracy for differential drive robot odometry compared to the Simplified Model (SM). However, the SM provides faster computation, making model selection critical for autonomous navigation tasks.

Area of Science:

Robotics
Control Systems
Autonomous Navigation

Background:

Precise modeling of differential drive robots is essential for autonomous systems.
Two distinct modeling approaches, Motor-Based Model (MBM) and Simplified Model (SM), are evaluated for four-wheel differential drive robots.

Purpose of the Study:

To comparatively analyze the accuracy and computational performance of MBM and SM for differential drive robot modeling.
To assess the impact of model selection on odometry accuracy and navigation task design.

Main Methods:

MBM utilizes four motor-specific transfer functions, while SM employs two transfer functions for linear and angular velocity.
Both models were validated against real odometry data from a SLAM system on a differential-drive robot.

Keywords:

differential robots identification motor-based model navigation simplified model

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Main Results:

MBM demonstrated higher accuracy in position and orientation estimation, with lower RMSE values compared to SM.
SM exhibited superior computational performance, achieving a 30% reduction in simulation time and lower memory usage.
MBM showed average position RMSE of 0.309 m vs. 0.414 m for SM; angular RMSE of 0.170 rad vs. 0.239 rad for SM.

Conclusions:

MBM is more suitable for applications demanding high precision, such as SLAM.
SM is advantageous for resource-constrained embedded systems requiring faster computation.
The trade-off between model complexity, accuracy, and computational cost is critical for selecting appropriate robot models.