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Updated: Jan 29, 2026

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

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Robust Model Predictive Control with a Dynamic Look-Ahead Re-Entry Strategy for Trajectory Tracking of

Diego Guffanti¹, Moisés Filiberto Mora Murillo^2,3, Santiago Bustamante Sanchez⁴

¹Centro de Investigación en Mecatrónica y Sistemas Interactivos-MIST, Universidad Indoamérica, Av. Machala y Sabanilla, Quito 170103, Ecuador.

Sensors (Basel, Switzerland)

|January 28, 2026

Summary

Accurate trajectory tracking for differential-drive mobile robots is improved with a Model Predictive Control (MPC) system. A novel re-entry strategy ensures stable path following even when the robot deviates, enhancing overall navigation robustness.

Keywords:

Model Predictive Control (MPC)differential-drive robot re-entry strategy robust navigation trajectory tracking

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Last Updated: Jan 29, 2026

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Published on: December 19, 2016

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Area of Science:

Robotics and Control Systems
Autonomous Navigation
Mobile Robot Dynamics

Background:

Accurate trajectory tracking is a critical challenge for differential-drive mobile robots (DDMRs) in real-world applications.
Model Predictive Control (MPC) offers a robust framework but struggles with significant path deviations.
Existing methods lack effective recovery mechanisms for maintaining stable tracking after deviations.

Purpose of the Study:

To experimentally validate an MPC controller integrated with a novel dynamic look-ahead re-entry strategy for a four-wheel DDMR.
To enhance the robustness and accuracy of DDMR trajectory tracking under perturbed conditions.
To quantitatively assess the performance improvements using metrics like RMSE and in-band percentage.

Main Methods:

Implemented an MPC controller with state and input constraints on linear (v) and angular (ω) velocities.
Integrated a SLAM algorithm with ROS 2 for real-time odometry correction.
Developed and tested a dynamic look-ahead re-entry strategy activated by lateral error, interpolating smooth recovery trajectories.

Main Results:

In nominal conditions, the best MPC configuration achieved a lateral RMSE of 0.05 m and heading RMSE of 0.06 rad, with 68.8% of the trajectory within the validation band.
Under perturbations, the proposed re-entry strategy significantly improved robustness, maintaining a lateral RMSE of 0.12 m and 51.4% in-band trajectory.
The re-entry strategy outperformed standard MPC by enabling faster recovery and reducing deviation magnitudes.

Conclusions:

The integration of MPC with the proposed dynamic look-ahead re-entry strategy substantially enhances both accuracy and robustness in DDMR trajectory tracking.
The spatially grounded recovery mechanism ensures consistent performance in challenging scenarios, crucial for reliable navigation.
This combined approach offers a promising solution for dependable mobile robot navigation in uncertain and dynamic environments.