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Development of a Particle Filter-Based Path Tracking Algorithm of Autonomous Trucks with a Single Steering and

Sehwan Kim1, Munjung Jang1, Hanbyeol La1

  • 1School of ICT, Robotics & Mechanical Engineering, Hankyong National University, Anseong-si 17579, Republic of Korea.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary

This study introduces a particle-filter and linear-quadratic-regulator (LQR) based path tracking algorithm for autonomous trucks. The LQR method demonstrated superior performance over sliding mode control, reducing lateral preview errors by 18%.

Keywords:
autonomous truckslinear quadratic regulatormonocular cameraparticle filterpath trackingsingle steering and driving module

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Area of Science:

  • Robotics and Automation
  • Computer Vision
  • Control Systems Engineering

Background:

  • Autonomous vehicle and automated guided vehicle (AGV) research focuses on enhancing safety and efficiency.
  • Current perception methods using AI and sensors require extensive data, posing limitations.
  • Effective path tracking necessitates environmental recognition and robust control strategies.

Purpose of the Study:

  • To develop and validate a novel path tracking algorithm for autonomous trucks using a monocular camera.
  • To compare the performance of the proposed Linear-Quadratic-Regulator (LQR) based controller against conventional methods.
  • To address the limitations of data-intensive AI perception methods in autonomous driving.

Main Methods:

  • A particle-filter-based algorithm was employed for target RGB recognition using a monocular camera.
  • Path tracking errors were calculated to derive an LQR-based desired steering angle.
  • Autonomous truck steering and driving were controlled via pulse-width-modulation (PWM) motors.

Main Results:

  • The LQR-based path tracking algorithm was successfully verified in three distinct evaluation scenarios.
  • Performance comparison against Sliding Mode Control (SMC) showed the LQR method's superiority.
  • The Root Mean Square (RMS) of the lateral preview error for SMC was approximately 18% greater than the LQR-based method.

Conclusions:

  • The proposed LQR-based path tracking controller offers improved accuracy and robustness for autonomous trucks.
  • This approach provides an effective alternative to data-heavy AI perception systems.
  • The study validates the feasibility of monocular camera-based path tracking for autonomous driving applications.