研究目的
Investigating the path guidance of logistics transport vehicles based on image recognition and image processing in port areas to reduce errors in cargo transportation.
研究成果
The study successfully develops a visual navigation system for logistics transport vehicles using image processing, with a novel centerline extraction algorithm. Simulations and experiments show feasibility, with an 89% success rate in navigation, but improvements are needed for adaptability, obstacle avoidance, and automation in power supply.
研究不足
The algorithm has poor adaptability in some conditions, such as strong sunlight causing image blur, obstacles not detected by ultrasonic sensors, and issues with road conditions like gullies. Power supply management requires manual intervention, and there are challenges with obstacle avoidance and environmental interferences.
1：Experimental Design and Method Selection:
The study uses image recognition and processing techniques for visual navigation of automatic transport vehicles. It involves mathematical modeling of vehicle kinematics, image preprocessing, edge detection using morphological operations, and centerline extraction. A fuzzy controller is designed for path tracking, and simulations are conducted in MATLAB/Simulink.
2：Sample Selection and Data Sources:
The research object is logistics transport vehicles in port areas. Experimental data includes images of ground guidance paths and simulation trajectories.
3：List of Experimental Equipment and Materials:
Not explicitly detailed in the paper, but implies the use of cameras for image capture, automatic transport vehicles, and computational tools like MATLAB.
4：Experimental Procedures and Operational Workflow:
Steps include capturing path images, preprocessing to remove noise, detecting edges, extracting the centerline, and simulating vehicle motion with a fuzzy controller. Real-world tests involve running the vehicle on straight and circular paths to measure errors.
5：Data Analysis Methods:
Data is analyzed using MATLAB for simulation and error analysis, with statistical evaluation of navigation success rates and error tolerances.
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