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Kalman Filter Applied To A Differential Robot

Sendey Vera, Luis Chuquimarca, Douglas Plaza

TL;DR

The paper tackles reliable localization and trajectory tracking for a differential-drive robot in a constrained environment. It combines a Kalman filter-based estimator with a PI controller and kinematic models to correct encoder-driven measurements in real time using MATLAB/Simulink and Arduino. Experimental results on a two-wheeled prototype with incremental encoders show improved speed-tracking, hexagonal-path execution, and reduced estimation error compared with PI control alone. The study also discusses encoder noise characteristics, tuning guidance, and practical considerations for implementing Kalman-state estimation in mobile robotics.

Abstract

This document presents the study of the problem of location and trajectory that a robot must follow. It focuses on applying the Kalman filter to achieve location and trajectory estimation in an autonomous mobile differential robot. The experimental data was carried out through tests obtained with the help of two incremental encoders that are part of the construction of the differential robot. The data transmission is carried out from a PC where the control is carried out with the Matlab/Simulink software. The results are expressed in graphs showing the path followed by the robot using PI control, the estimator of the Kalman filter in a real system.

Kalman Filter Applied To A Differential Robot

TL;DR

The paper tackles reliable localization and trajectory tracking for a differential-drive robot in a constrained environment. It combines a Kalman filter-based estimator with a PI controller and kinematic models to correct encoder-driven measurements in real time using MATLAB/Simulink and Arduino. Experimental results on a two-wheeled prototype with incremental encoders show improved speed-tracking, hexagonal-path execution, and reduced estimation error compared with PI control alone. The study also discusses encoder noise characteristics, tuning guidance, and practical considerations for implementing Kalman-state estimation in mobile robotics.

Abstract

This document presents the study of the problem of location and trajectory that a robot must follow. It focuses on applying the Kalman filter to achieve location and trajectory estimation in an autonomous mobile differential robot. The experimental data was carried out through tests obtained with the help of two incremental encoders that are part of the construction of the differential robot. The data transmission is carried out from a PC where the control is carried out with the Matlab/Simulink software. The results are expressed in graphs showing the path followed by the robot using PI control, the estimator of the Kalman filter in a real system.
Paper Structure (10 sections, 10 equations, 13 figures)

This paper contains 10 sections, 10 equations, 13 figures.

Table of Contents

  1. Introduction
  2. MECHANICAL DESIGN
  3. Kinematic models of the robot
  4. ELECTRONICS
  5. SOFWARE
  6. CONTROL SYSTEM
  7. Kalman filter
  8. Activation of the robot for the hexagon path with PID + FK control.
  9. CONCLUSIONS
  10. REFERENCES

Figures (13)

  • Figure 1: Angular speed wl and wr, angular and linear direction of the robot.
  • Figure 2: Direct kinematic model diagram of the robot.
  • Figure 3: Inverse kinematic model diagram of the robot.
  • Figure 4: The interconnection of elements between the plant and the computer with the Matlab-Simulink interface.
  • Figure 5: The graphical programming made in Matlab/Simulink.
  • ...and 8 more figures