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Research on Reinforcement Learning-Based Autonomous Navigation and Obstacle Avoidance Methods for AGVs in Unknown

Tianye Luo¹, Jing Hu¹, Bangcheng Zhang²

¹School of Mechatronical Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)

|June 12, 2026

Summary

This summary is machine-generated.

This study introduces BEAGM-PPO, a reinforcement learning framework for hospital automated guided vehicles (AGVs) that improves navigation efficiency. The novel approach enhances sample efficiency and convergence, paving the way for safer hospital environments.

Keywords:

AGV imitation learning obstacle avoidance reinforcement learning

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Area of Science:

Robotics
Artificial Intelligence
Machine Learning

Background:

Reinforcement learning (RL) offers potential for autonomous navigation in hospital automated guided vehicles (AGVs).
Challenges include designing reward functions, low sample efficiency, and slow convergence.
Existing methods struggle with the complexities of unknown hospital environments.

Purpose of the Study:

To propose BEAGM-PPO, a novel RL framework for autonomous navigation in hospital AGVs.
To enhance sample efficiency and convergence speed in RL algorithms.
To address the limitations of current autonomous navigation systems in healthcare settings.

Main Methods:

Developed BEAGM-PPO, incorporating a reference model with expert demonstrations and policy derivation.
Utilized behavior cloning and uncertainty estimation for policy derivation.
Integrated ant colony optimization (ACO)-inspired pheromones and memory replay for improved exploration and action selection.

Main Results:

BEAGM-PPO achieved the highest arrival rate in 3D simulation scenarios compared to baseline models.
The imitation learning approach enabled uncertainty estimation for policy and model.
Expanded training datasets further improved the performance of the autonomous navigation system.

Conclusions:

BEAGM-PPO provides a robust framework for autonomous navigation in hospital AGVs.
The method effectively improves sample efficiency and convergence behavior.
This research lays a foundation for advanced autonomous systems in hospital logistics and patient care.