In my third-to-fourth year of my bachelor’s degree, it is compulsory for my course to take a capstone course, called an Integrated Design Project. It is a project that spans over two semesters, and gives us the opportunity to demonstrate the knowledge and skills that we’ve learnt throughout our studies. It is different from a Final Year Project, as this is a group-based work.
In my group’s case, at the supervision of our group’s supervisor Dr Chang, we were requested by a company in Sri Kembangan, Selangor which specializes in manufacturing Gondola shelves and supermarket racks to provide a smart shelving system which is able to calculate the remaining stock on the shelf and logs them in a real-time basis. The requirement by the company was that we weren’t allowed to use any cameras to track stock movement. We had about seven months to design and implement a prototype, as well as complete a proposal and a presentation for our solution. A total of four of us, with me leading the Hardware and Software implementation of the shelf, started work as soon as was possible.
Once we found out the size of our prototype shelf, we came up with two solutions to our design; by using a weight sensor and an RFID reader. The two solutions that we provide is to allow for a pros and cons comparison between them. Once all the electronics have arrived, I started to work on the hardware and software designs for our prototype.
Mini shelf sponsored by our client for our prototype
Weight Sensor System
Firstly, I had to figure out how to attach the weight sensor onto the shelf. The four corners that’s holding up the entire shelf is screwed in with M.6 hex bolts and the weight sensors couldn’t fit on. Therefore, I had to design and 3D print an attachment part that allows for a snug fit between the weight sensor and the shelf. The parts are designed in Autodesk Fusion 360.
Once the parts are printed, and the weight sensors are attached on the four corners of the shelf, it was time to connect them up and start coding. The weight sensors are connected up to a HX711 Analog-to-Digital converter and the output of the HX711 is wired up to a Raspberry pi 4. After the testing was done, I started on the code. I programmed the raspberry pi to read the weight data from the shelf, divide that by the weight of one item on the shelf to get an approximate number of items on the shelf. I then programmed it to write the data to a Google Sheet page whenever there was a change in item quantity. I also connected an Oled screen to the raspberry pi to show the number of items on the shelf. The cons of using a weight sensor then became apparent – you can only measure one type of item with similar weights. However, this was not much of a problem as these weight sensors are rated to a high load and can be used in a manufacturing and shipping environment in which the products weight similarly in bulk.
Raspberry pi wired to the weight sensor with a HX711 ADC
The results we obtained was quite satisfactory. The weight sensors, although it may handle a high load (up to 200kg per sensor), does not lose its accuracy when measuring lighter loads. We were able to measure the weight changes up to ±5g accurately and update the google sheet in real time. One of my groupmate also developed an app to track the stock movements.
Here’s a short video of our initial system testing using the weight sensor:
RFID System
Next, I had to program for the RFID system of our project. The RFID reader was generously borrowed to us by another company specializing in RFID and IoT development. The RFID reader that they borrowed us did not come with a programmer’s manual and only had a ‘demo’ program. Hence, I had to think of a way to read the data off of the demo program and into my program.
I ended up using a python library called the PyTesseract, which is an optical character recognition library that can help me extract the valuable data from the demo program. I coded my program to take a screenshot of this demo program when it’s running, crop/resize it, and applied some filters to allow for the library to accurately recognize the unique ID (UID) of each RFID tags. The recognized UID can then be compared to a database to find out what product that UID is from. Once that was fully working, I had to program it to look for changes in the stock movement so that it may log it in Google Sheet which can be accessed through the same app that my groupmate created.
To attach the RFID antenna on the shelf itself, I had to design another part in Fusion 360. This part was made at an angle to ensure that the antenna has a maximum area of coverage with the tags. A magnet is embedded in the part to allow for the antenna to stick on the metallic wall of the shelf.
PCB design
Once we got everything working as intended, we wanted to take it a step further and develop our own PCB to reduce all the wirings. So we designed our board in Autodesk Eagle and produced the PCB in the University’s PCB fabrication lab.
Unfortunately, after very thorough and time consuming testing and debugging, we couldn’t get the PCB to work. We suspect there might be stray capacitances and inductances within the traces which cause erroneous readings on the output. We finally opted to go back to the breadboard version due to the lack of time.
Here’s a demo video of our completed Weight sensor and RFID sensor system:
Additional Features
After the main hardware and software components of the shelf is done, it was time to add some additional features to make the shelf ‘smart’. Initially, we planned to implement a neural network to predict the sales data of future sales given the current sale. However, due to the lack of time and insufficient datasets, we opted for a pseudo-AI system instead. We were able to achieve four additional features with our prototype which includes:-
- An android application
- Weather sales prediction
- Holiday sales prediction
- Google Trends prediction
Android Application
We’ve developed an app which allows a user to see the current product stock on the shelf which is meant for customers. We thought that at the current rampant stage of the covid-19 pandemic (at the time of this post), customers would more likely buy from a store in which they can check has their products in stock. This can also promote social distancing between the customers as they can check whether their items are in stock before going out to the store.
Holiday and Weather Sales Prediction
I’ve also coded a local holiday and a weather forecaster in our program, which is able to forecast the weather for the entire day as well as the local holiday for the next entire year. The program is able to make a pseudo-prediction on what the store should stock up on. These predictions will be updated every time the program is launched. In the future, if given enough time and sales data, there is a potential for true AI prediction that could be done based on previous holiday sales and weather data.
Google Keyword Analysis
I’ve also added a Google keyword trendline which is based on Google keyword searches in an area. It compares the frequency of searches of different keywords and plots the frequency in a frequency-time graph. For example:
With the graph obtained above which shows the frequency of searches of laptop models; if you were a computer salesperson and wanted to set up shop in Malaysia, it is clear that Asus is the more searched laptop brand in Malaysia and it’s more likely that the sales of Asus will be higher than the other laptops. The program will update this graph each time it’s launched with real-time data.
Conclusion
After close to 7 months, we finally have a finished a working prototype. This project was chosen as one of the finalists in our university’s Best Capstone Project awards. The code of this project can be found in my Github page. This code will not work straight out-of-the-box as I’ve removed some personal API keys and files from the project but this project can be used as a reference for anyone interested in doing something similar. Thanks for reading!
