Research on AGV trackless guidance technology based on the global vision

Area of Science:

• Robotics and Automation
• Computer Vision
• Manufacturing Engineering

Background:

• The manufacturing industry is transitioning towards intelligent and flexible production models.
• Automatic Guided Vehicles (AGVs) are crucial for modernizing factories and enabling efficient material handling.
• Traditional AGV guidance systems can be inflexible and costly to implement.

Purpose of the Study:

• To propose and evaluate a novel trackless guidance technology for AGVs.
• To enhance AGV navigation capabilities using global vision and intelligent algorithms.
• To demonstrate the feasibility of a flexible, precise, and cost-effective AGV guidance solution.

Main Methods:

• Utilizing a global vision camera to capture AGV driving area imagery.
• Employing image processing techniques to extract obstacle and AGV position data.
• Implementing the A* algorithm for intelligent path planning.
• Leveraging wireless network communication for real-time AGV control.

Main Results:

• The proposed global vision-based AGV guidance system was experimentally validated.
• The system demonstrated high flexibility, precision, and cost-effectiveness.
• The technology showed strong scalability for various industrial applications.

Conclusions:

• The developed trackless guidance technology is a feasible solution for AGV navigation.
• This approach offers significant advantages for the implementation of intelligent warehouses and unmanned chemical plants.
• The system contributes to the advancement of smart manufacturing and automated logistics.