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Updated: Sep 11, 2025

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Design a path - planning strategy for mobile robot in multi-structured environment based on distributional

Anh-Tu Nguyen1, Duc-Duy Pham1, Van-Nghia Le1

  • 1School of Mechanical and Automotive Engineering, Hanoi University of Industry, No. 298, Cau Dien Street, Bac Tu Liem District, 100000, Vietnam.

Methodsx
|August 18, 2025
PubMed
Summary

This study introduces a new path-planning strategy for mobile robots using Lightweight Learned Image Denoising with Instance Adaptation (LIDIA) and Quantile Regression Deep Q-Network (QR-DQN) for safer navigation in unknown environments.

Keywords:
Distributional reinforcement learningMobile robotPath – planningQR – DQN NetworkSub – reward mechanism

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computer Vision

Background:

  • Mobile robots require robust navigation strategies for unknown environments.
  • Image noise in depth data hinders accurate environmental perception.
  • Existing path-planning methods struggle with complex, multi-object scenarios.

Purpose of the Study:

  • To develop a novel, integrated path-planning framework for mobile robots in unknown environments.
  • To enhance robot navigation capabilities by improving depth image quality and decision-making under uncertainty.
  • To enable collision-free navigation in complex, multi-object environments.

Main Methods:

  • Integration of Lightweight Learned Image Denoising with Instance Adaptation (LIDIA) for depth image denoising and calibration.
  • Application of Quantile Regression Deep Q-Network (QR-DQN), a distributional reinforcement learning model, for path-planning.
  • Incorporation of a sub-goal mechanism to manage complex navigation tasks.

Main Results:

  • The proposed framework effectively denoises and calibrates depth images using LIDIA.
  • QR-DQN successfully generates smooth, short-distance paths, outperforming other methods in various scenarios.
  • The integrated approach enables effective collision-free navigation in sophisticated environmental conditions.

Conclusions:

  • The combined LIDIA and QR-DQN framework offers a significant advancement in mobile robot path-planning.
  • This strategy enhances robot autonomy and safety in complex, unmapped environments.
  • The method demonstrates superior performance in achieving efficient and smooth path-planning.