Warehouse Phased Array (PHRAY) Gun
This project was completed in May of 2021 and represents the culmination of a collaborative team effort to fulfill our Electrical Engineering undergraduate senior capstone project requirement at California State University Sonoma.
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General Project Details
The problem we addressed was warehouses’ need to detect and keep track of storage containers. Tracking containers and the items within them is crucial to maintaining accurate inventory and location information, which also impacts the speed at which warehouses can ship items. Our solution involved leveraging radio frequency (RF) technology and machine learning (ML) techniques to design an electronically scanning phased array system. This handheld, electronically steerable phased array device, or PHRAY gun, can scan for multiple objects in a single use, unlike traditional barcode scanners that scan one object at a time. Designing this system posed the primary challenge of controlling each antenna characteristic within the multiple-element, non-uniformly spaced linear array (non-USLA) needed to perform electronic scanning. Since this is an inverse problem, where the desired outcome is known but selecting the appropriate input parameters to achieve this outcome is complex, we implemented ML techniques, or specifically Genetic Algorithms, to optimize the construction of the non-uniformly spaced, phased antenna array.
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Overall, we successfully optimized and prototyped a proof of concept for remotely detecting and tracking objects containing radio-frequency identification (RFID) tags. The PHRAY gun, a handheld device measuring roughly 1 cubic foot and weighing approximately 7.2 pounds, offers users the choice between "manual steering" and "automatic scanning" modes to identify the location of RFID tags up to 14 inches away, displaying directional information (e.g., East, Northeast, North, Northwest, West) and quantity of the detected RFID tags on a Liquid Crystal Display (LCD) screen. The PHRAY gun reads Electronic Product Codes (EPC) and Received Signal Strength Indicators (RSSI) from detected tags, providing comprehensive scanning results based on user-selected or programmed scanning directions. This experience enabled us to enhance our skills across a broad spectrum of essential electrical engineering topics, including Array Factor computations, machine learning techniques, MATLAB and Python programming, RFID readers, operational amplifiers, isolators, antennas, LCDs, transmission lines, step-up boosters, PCB design using Eagle, 3D printing with SolidWorks, utilization of Vector Network Analyzers (VNA) and Spectrum Analyzers, soldering, collaboration, budget analysis, project management, client interaction, and the derivation of marketing and engineering requirements, and more.
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For more information about our team, please see the About Us tab.
For more detailed technical information, please see the Documentation tab.