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# Title Team Members TA Documents Sponsor
7 Microcontroller-based Occupancy Monitoring (MOM)
Franklin Moy
John Li
Vish Gopal Sekar
Hanyin Shao design_document1.pdf
final_paper1.pdf
photo1.jpg
photo2.jpg
presentation1.pdf
proposal1.pdf
video
# Microcontroller-based Occupancy Monitoring (MOM)

Team Members:
- Franklin Moy (fmoy3)
- Vish Gopal Sekar (vg12)
- John Li (johnwl2)

# Problem

With the campus returning to normalcy from the pandemic, most, if not all, students have returned to campus for the school year. This means that more and more students will be going to the libraries to study, which in turn means that the limited space at the libraries will be filled up with the many students who are now back on campus. Even in the semesters during the pandemic, many students have entered libraries such as Grainger to find study space, only to leave 5 minutes later because all of the seats are taken. This is definitely a loss not only to someone's study time, but maybe also their motivation to study at that point in time.

# Solution

We plan on utilizing a fleet of microcontrollers that will scan for nearby Wi-Fi and Bluetooth network signals in different areas of a building. Since students nowadays will be using phones and/or laptops that emit Wi-Fi and Bluetooth signals, scanning for Wi-Fi and Bluetooth signals is a good way to estimate the fullness of a building. Our microcontrollers, which will be deployed in numerous dedicated areas of a building (called sectors), will be able to detect these connections. The microcontrollers will then conduct some light processing to compile the fullness data for its sector. We will then feed this data into an IoT core in the cloud which will process and interpret the data and send it to a web app that will display this information in a user-friendly format.

# Solution Components

## Microcontrollers with Radio Antenna Suite

Each microcontroller will scan for Wi-Fi and Bluetooth packets in its vicinity, then it will compile this data for a set timeframe and send its findings to the IoT Core in the Cloud subsystem. Each microcontroller will be programmed with custom software that will interface with its different radio antennas, compile the data of detected signals, and send this data to the IoT Core in the Cloud subsystem.

The microcontroller that would suit the job would be the ESP32. It can be programmed to run a suite of real-time operating systems, which are perfect for IoT applications such as this one. This enables straightforward software development and easy connectivity with our IoT Core in the Cloud. The ESP32 also comes equipped with a 2.4 GHz Wi-Fi transceiver, which will be used to connect to the IoT Core, and a Bluetooth Low Energy transceiver, which will be part of the radio antenna suite.

Most UIUC Wi-Fi access points are dual-band, meaning that they communicate using both the 2.4 GHz and 5 GHz frequencies. Because of this, we will need to connect a separate dual-band antenna to the ESP32. The simplest solution is to get a USB dual-band Wi-Fi transceiver, such as the TP-Link Nano AC600, and plug it into a USB Type-A breakout board that we will connect to each ESP32's GPIO pins. Our custom software will interface with the USB Wi-Fi transceiver to scan for Wi-Fi activity, while it will use the ESP32's own Bluetooth Low Energy transceiver to scan for Bluetooth activity.

## Battery Backup

It is possible that the power supply to a microcontroller could fail, either due to a faulty power supply or by human interference, such as pulling the plug. To mitigate the effects that this would have on the system, we plan on including a battery backup subsystem to each microcontroller. The battery backup subsystem will be able to not only power the microcontroller when it is unplugged, but it will also be able to charge the battery when it is plugged in.

Most ESP32 development boards, like the Adafruit HUZZAH32, have this subsystem built in. Should we decide to build this subsystem ourselves, we would use the following parts. Most, if not all, ESP32 microcontrollers use 3.3 volts as its operating voltage, so utilizing a 3.7 volt battery (in either an 18650 or LiPo form factor) with a voltage regulator would supply the necessary voltage for the microcontroller to operate. A battery charging circuit consisting of a charge management controller would also be needed to maintain battery safety and health.

## IoT Core in the Cloud

The IoT Core in the Cloud will handle the main processing of the data sent by the microcontrollers. Each microcontroller is connected to the IoT Core, which will likely be hosted on AWS, through the ESP32's included 2.4GHz Wi-Fi transceiver. We will also host on AWS the web app that interfaces with the IoT Core to display the fullness of the different sectors. This web app will initially be very simple and display only the estimated fullness. The web app will likely be built using a Python web framework such as Flask or Django.


# Criterion For Success

- Identify Wi-Fi and Bluetooth packets from a device and distinguish them from packets sent by different devices.
- Be able to estimate the occupancy of a sector within a reasonable margin of error (15%), as well as being able to compute its fullness relative to that sector's size.
- Display sector capacity information on the web app that is accurate within 5 minutes of a user accessing the page.
- Battery backup system keeps the microcontroller powered for at least 3 hours when the wall outlet is unplugged.

Decentralized Systems for Ground & Arial Vehicles (DSGAV)

Mingda Ma, Alvin Sun, Jialiang Zhang

Featured Project

# Team Members

* Yixiao Sun (yixiaos3)

* Mingda Ma (mingdam2)

* Jialiang Zhang (jz23)

# Problem Statement

Autonomous delivery over drone networks has become one of the new trends which can save a tremendous amount of labor. However, it is very difficult to scale things up due to the inefficiency of multi-rotors collaboration especially when they are carrying payload. In order to actually have it deployed in big cities, we could take advantage of the large ground vehicle network which already exists with rideshare companies like Uber and Lyft. The roof of an automobile has plenty of spaces to hold regular size packages with magnets, and the drone network can then optimize for flight time and efficiency while factoring in ground vehicle plans. While dramatically increasing delivery coverage and efficiency, such strategy raises a challenging problem of drone docking onto moving ground vehicles.

# Solution

We aim at tackling a particular component of this project given the scope and time limitation. We will implement a decentralized multi-agent control system that involves synchronizing a ground vehicle and a drone when in close proximity. Assumptions such as knowledge of vehicle states will be made, as this project is aiming towards a proof of concepts of a core challenge to this project. However, as we progress, we aim at lifting as many of those assumptions as possible. The infrastructure of the lab, drone and ground vehicle will be provided by our kind sponsor Professor Naira Hovakimyan. When the drone approaches the target and starts to have visuals on the ground vehicle, it will automatically send a docking request through an RF module. The RF receiver on the vehicle will then automatically turn on its assistant devices such as specific LED light patterns which aids motion synchronization between ground and areo vehicles. The ground vehicle will also periodically send out locally planned paths to the drone for it to predict the ground vehicle’s trajectory a couple of seconds into the future. This prediction can help the drone to stay within close proximity to the ground vehicle by optimizing with a reference trajectory.

### The hardware components include:

Provided by Research Platforms

* A drone

* A ground vehicle

* A camera

Developed by our team

* An LED based docking indicator

* RF communication modules (xbee)

* Onboard compute and communication microprocessor (STM32F4)

* Standalone power source for RF module and processor

# Required Circuit Design

We will integrate the power source, RF communication module and the LED tracking assistant together with our microcontroller within our PCB. The circuit will also automatically trigger the tracking assistant to facilitate its further operations. This special circuit is designed particularly to demonstrate the ability for the drone to precisely track and dock onto the ground vehicle.

# Criterion for Success -- Stages

1. When the ground vehicle is moving slowly in a straight line, the drone can autonomously take off from an arbitrary location and end up following it within close proximity.

2. Drones remains in close proximity when the ground vehicle is slowly turning (or navigating arbitrarily in slow speed)

3. Drone can dock autonomously onto the ground vehicle that is moving slowly in straight line

4. Drone can dock autonomously onto the ground vehicle that is slowly turning

5. Increase the speed of the ground vehicle and successfully perform tracking and / or docking

6. Drone can pick up packages while flying synchronously to the ground vehicle

We consider project completion on stage 3. The stages after that are considered advanced features depending on actual progress.

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