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Road-Adaptive Precise Path Tracking Based on Reinforcement Learning Method.

Sensors (Basel, Switzerland)ยท2025
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Related Experiment Video

Updated: Jan 15, 2026

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking FLLIT
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Lightweight Road Adaptive Path Tracking Based on Soft Actor-Critic RL Method.

Yubo Weng1, Jinhong Sun2

  • 1Beijing-Dublin International College Electronic Information Engineering, Beijing University of Technology, Beijing 100124, China.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a speed-adaptive robot path-tracking system using adaptive soft actor-critic (ASAC) and Stanley methods. The framework achieves accurate path following by dynamically adjusting speed and steering based on road and vehicle conditions.

Keywords:
Stanley methodpath trackingroad surface adaptiveroad surface detectionsoft actor-criticspeed-adaptive

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Control Systems

Background:

  • Accurate robot path tracking is crucial for autonomous systems.
  • Existing methods often struggle with dynamic speed adaptation and varying road conditions.

Purpose of the Study:

  • To develop a speed-adaptive robot path-tracking framework for enhanced accuracy.
  • To integrate Lidar-Inertial Odometry Simultaneous Localization and Mapping (LIO-SLAM) for precise localization.
  • To leverage adaptive soft actor-critic (ASAC) for dynamic control adjustments.

Main Methods:

  • Utilized LIO-SLAM for 100 Hz robot pose estimation.
  • Employed Rapidly exploring Random Tree (RRT) for global path planning and A* for local obstacle avoidance.
  • Integrated U-Net for road surface classification and ASAC for adaptive speed and steering control via the Stanley method.

Main Results:

  • The proposed STANLY_ASAC framework demonstrated accurate path following in diverse scenarios.
  • Adaptive control adjusted vehicle acceleration and lateral deviation gain effectively.
  • Road surface classification improved tracking accuracy by compensating for coefficient errors.

Conclusions:

  • The speed-adaptive framework offers robust and accurate robot path tracking.
  • Adaptive control based on road and vehicle state is key to performance.
  • The integration of LIO-SLAM, RRT, A*, U-Net, ASAC, and Stanley methods provides a comprehensive solution.