Web application to control a swarm of Raspberry Pis

A web-based solution to control a swarm of Raspberry Pis, featuring a real-time dashboard, a deep learning inference engine, 1-click Cloud deployment, and dataset labeling tools.

I recently completed an AI mentorship program at SharpestMinds, of which the central element was to build a project, or even better, a complete product. I choose the latter, and in this article, I write about what I built, how I built it, and what I learned along the way.

Continued from Part 1…

This is the second article of the three-part SorterBot series.

• Part 1 — General project description and the Web Application
• Part 2 — Controlling the Robotic Arm
• Part 3 — Transfer Learning and Cloud Deployment

Source code on GitHub:

• Control Panel: Django backend and React frontend, running on EC2
• Inference Engine: Object Recognition with PyTorch, running on ECS
• Raspberry: Python script to control the Robotic Arm
• Installer: AWS CDK, GitHub Actions and a bash script to deploy the solution
• LabelTools: Dataset labeling tools with Python and OpenCV

The Robotic Arm

The robot, before assembly (Photo by Author)

The robot arm arrived from AliExpress, it was without a specific brand, advertised as a DIY toy, which made it an affordable option, costing me only $118 (+$40 in tariffs). Since gripping objects with a robot arm requires a lot of precision, which I could not possibly expect from an arm in this price category, I decided to use a magnet to move the objects to the containers. I ordered one from Grove for $11, specifically designed for Raspberry Pi. It came with its own control electronics, so a GPIO (General Purpose Input/Output) pin could be used to turn it off and on. For the camera, I purchased a Pi NoIR Camera V2 for $45, which was also pretty easy to set up. To run my software and control all the above devices, I bought the latest version of the Raspberry Pi, which is the Raspberry Pi 4 Model B with 4 GB of RAM. I ordered it in a package, together with an SD card, housing, heat sinks, and power adapter, for $130. The hardware cost me $344 in total.

robotics deep-learning aws computer-vision raspberry-pi deep learning

A few compelling reasons for you to starting learning Computer. In today’s world, Computer Vision technologies are everywhere.

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Part 1 of the series looked at representation learning and how self-supervised learning can alleviate the problem of data inefficiency in learning representations of images.

Implementations regarding all of above experiments alongside the different result plots are provided in GitHub repository.

Deep Computer Vision is capable of doing object detection and image classification task. In image classification tasks, the particular system receives some input image.