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Updated: Sep 11, 2025

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Research on AGV Path Planning Based on Improved DQN Algorithm.

Qian Xiao1, Tengteng Pan1, Kexin Wang1

  • 1School of Intelligent Science Information Engineering, Shenyang University, Shenyang 110044, China.

Sensors (Basel, Switzerland)
|August 14, 2025
PubMed
Summary
This summary is machine-generated.

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A Social Force Evacuation Model with Guides Based on Fuzzy Clustering and a Two-Layer Fuzzy Inference.

Computational intelligence and neuroscience·2022
See all related articles

This study introduces the B-PER Deep Q Network (DQN) algorithm for adaptive path planning in Automated Guided Vehicle (AGV) systems. The new method enhances convergence speed and adaptability in complex environments, overcoming limitations of traditional deep reinforcement learning.

Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional deep reinforcement learning (DRL) methods exhibit slow convergence and poor adaptability in complex environments.
  • These methods often lead to suboptimal solutions (local optima) in Automated Guided Vehicle (AGV) system applications.

Purpose of the Study:

  • To propose an improved adaptive path planning algorithm for AGV systems using a novel Deep Q Network (DQN) approach.
  • To enhance the efficiency, adaptability, and convergence speed of DRL-based path planning.

Main Methods:

  • Developed the B-PER Deep Q Network (DQN) algorithm, incorporating a dynamic temperature adjustment mechanism for Boltzmann strategy.
  • Integrated a Priority Experience Replay (PER) mechanism for improved training efficiency and task diversity.
Keywords:
automatic guided vehicledeep Q networkdeep reinforcement learningpath planning

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  • Designed a refined multi-objective reward function including direction guidance, step punishment, and endpoint reward.

    • Main Results:

    • The B-PER DQN algorithm demonstrated a higher success rate compared to other algorithms in the same environment.
    • Achieved faster convergence speeds, indicating improved learning efficiency.
    • Validated the algorithm as an efficient and adaptive solution for complex path planning tasks.

    Conclusions:

    • The proposed B-PER DQN algorithm effectively addresses the limitations of traditional DRL methods in AGV path planning.
    • The adaptive mechanisms and refined reward function contribute to superior performance in complex and dynamic environments.
    • This research offers a promising approach for intelligent path planning in autonomous systems.