Project
| # | Title | Team Members | TA | Documents | Sponsor |
|---|---|---|---|---|---|
| 40 | Automated guided vehicle for cargo delivery in factories |
Qiqian Fu Xuhong He Yuyi Ao Zhengjie Wang |
design_document1.pdf final_paper1.pdf final_paper2.pdf proposal1.pdf |
Jiahuan Cui | |
| # Members - Zhengjie Wang[zw65] - Xuhong He[xuhongh2] - Yuyi Ao[yuyiao2] - Qiqian Fu[qiqianf2] # Problem Cargo delivery has long been a problem in large factories due to the low efficiency, restricted working period, and high safety risks caused by manual labor. Human-operated vehicles or manual transport methods often result in delays, errors, and inconsistent performance, especially in complex factory surroundings. Workers’ working hours are limited and therefore cannot maintain a 24/7 operation. Additionally, heavy machinery, narrow transporting channels, and dynamic obstacles increase the risk of accidents and injuries. To solve these problems, factory owners and researchers want to design a kind of automated guided vehicle that performs better than manual labor. # Solution Overview An automated guided vehicle needs to be designed and assembled. The vehicle needs to deliver cargo within a large factory. The vehicle needs to be equipped with a control/navigation and obstacle avoidance system. This system ensures that the vehicle can move to the ordered destination by itself safely. Moreover, the vehicle needs to lift goods for at least 10 kilograms. This AGV frees workers from moving the goods from point to point, and the only job they need to do is to place the goods in the correct position when the AGV reaches its destination. # Solution Components ## Automated motion control mechanisms - Mechanical systems controlling motions that contain lifting, turning, and acceleration. - Signal transfer system transferring analog signal to digital ones. ## Path planning and efficient communication - Path planning that enables the vehicle to navigate efficiently in the factory - Efficient communication protocol for vehicle and to receive instructions from central control system ## Obstacle detection and stopping - Lidar-based detection system for object recognition and stopping. - PointPillars network for 3D cloud point processing to detect. ## Map Reconstrucion and Localization - Using SLAM algorithms to reconstruct the scene. - Localization of the vehicle in the given route. ## Criterion for Success - Basic motions of lifting, turning, and acceleration can be accomplished by the mechanical system - Following designated path and be able to shift the cargo to the correct shelf - The vehicle can successfully stop when an obstacle is detected within the defined range. - The scene in the factory can be correctly constructed and the vehicle can be localized. |
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