Outdoor Warehouse Management: UAS-Driven Precision Tracking of Stacked Steel Bars

Area of Science:

• Robotics and Automation
• Computer Vision
• Logistics and Supply Chain Management

Background:

• Outdoor product identification faces challenges like variable lighting, weather, and lack of fixed infrastructure.
• Existing methods like GPS and RFID often require external infrastructure, limiting scalability in unstructured environments.

Purpose of the Study:

• To develop a vision-based unmanned aerial vehicle (UAV) system for autonomous product localization in outdoor industrial settings.
• To create an infrastructure-free method for accurate product positioning using QR code detection and relative spatial relationships.

Main Methods:

• A UAV autonomously scans areas, capturing video frames to detect and extract QR codes.
• Spatial relationships between products are computed using a trust-ability graph for enhanced reliability.
• The system relies on relative positioning, eliminating the need for external infrastructure like GPS or RFID.

Main Results:

• The proposed algorithm achieved over 94% positioning accuracy in indoor settings and 80% in outdoor environments.
• High accuracy was maintained even under conditions of occlusion and varying illumination.
• The system demonstrated scalability and adaptability in real-world industrial scenarios.

Conclusions:

• The developed UAV system offers a novel, infrastructure-free approach to product localization in challenging environments.
• Integration of trust-ability scoring improves the reliability of product position detection.
• This technology has significant potential for enhancing automation in logistics and warehouse inventory management.