Navigating Unstructured Space: Deep Action Learning-Based Obstacle Avoidance System for Indoor Automated Guided Vehicles

Automated guided vehicles (AGVs) have become prevalent over the last decade. However, numerous challenges remain, including path planning, security, and the capacity to operate safely in unstructured environments. This study proposes an obstacle avoidance system that leverages deep action learning (...

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Bibliographic Details
Published inElectronics (Basel) Vol. 13; no. 2; p. 420
Main Authors Aryanti, Aryanti, Wang, Ming-Shyan, Muslikhin, Muslikhin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.01.2024
Subjects
Action learning
Artificial intelligence
Automated guided vehicles
Automatic guided vehicles
Automation
Automobiles
Cameras
Collision avoidance
Consumption
Decision making
Electronics in navigation
Image processing
Industry 4.0
Internet of Things
Learning
Localization
Machine learning
Navigation
Navigation systems
Neural networks
Object recognition
Obstacle avoidance
Order picking
Robotics
Systems design
Warehouses
Taiwan
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Summary:Automated guided vehicles (AGVs) have become prevalent over the last decade. However, numerous challenges remain, including path planning, security, and the capacity to operate safely in unstructured environments. This study proposes an obstacle avoidance system that leverages deep action learning (DAL) to address these challenges and meet the requirements of Industry 4.0 for AGVs, such as speed, accuracy, and robustness. In the proposed approach, the DAL is integrated into an AGV platform to enhance its visual navigation, object recognition, localization, and decision-making capabilities. Then DAL itself was introduced to combine the work of You Only Look Once (YOLOv4), speeded-up robust features (SURF), and k-nearest neighbor (kNN) and AGV control in indoor visual navigation. The DAL system triggers SURF to differentiate two navigation images, and kNN is used to verify visual distance in real time to avoid obstacles on the floor while searching for the home position. The testing findings show that the suggested system is reliable and fits the needs of advanced AGV operations.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics13020420