Project

# Title Team Members TA Documents Sponsor
34 Board Buddy
 Alfredo Angel
Gabe Valles
Louie Conn
 Surya Vasanth design_document1.pdf
final_paper1.pdf
grading_sheet1.pdf
photo1.jpg
presentation1.pdf
proposal1.pdf
video
# Board Buddy

## Team Members:
- Alfredo Angel (alfredo9)
- Gabriel Valles (gvall4)
- Lewis Conn (lewisc2)

# Problem

Instructional writing boards, such as chalkboards and whiteboards, are widely used in educational and professional settings, but manually erasing these boards is time-consuming and disrupts workflow. During brainstorming sessions, lectures, or meetings, manually erasing the board can slow down productivity. Additionally, custodians spend hours cleaning boards outside of school hours, making it a labor-intensive task. Current solutions are either completely manual or require expensive, rail-based automated systems that only work on pre-sized boards.

There is a need for a cost-effective, portable, and efficient automated eraser that can remotely clean boards of various sizes and shapes with minimal human intervention.

# Solution

We propose **BoardBuddy**, an autonomous board eraser designed to clean magnetic writing boards efficiently. The device will attach securely using **neodymium magnets** and navigate using **omnidirectional wheels** controlled by an **ESP32 microcontroller**. It will feature **edge detection using microswitch lever arms** and **an accelerometer for stability**.

The system will be battery-powered with a **low dropout regulator (LDO)** for stable operation. It will include a **lightweight 3D-printed housing** for structural support and protection. Additionally, a **mobile application** will allow users to remotely activate the device, schedule cleaning sessions, and monitor its status.

# Solution Components

## Subsystem 1: Locomotion and Mounting

**Function:** Enables the device to move smoothly across the board while maintaining consistent contact for effective erasing.

**Components:**
- **Omnidirectional Wheels** – Allow unrestricted movement in any direction.
- **Neodymium Magnets** – Securely mount the device while allowing mobility.
- **DC Motors** – High-torque motors for smooth movement (e.g., **Pololu 25D Metal Gearmotor 12V**).
- **Motor Drivers** – Dual-channel motor drivers (e.g., **TB6612FNG**).

## Subsystem 2: Erasing Mechanism

**Function:** Erases the board as the device moves.

**Components:**
- **Eraser Pads** – Replaceable pads mounted on the device.
- **Spring Mechanism** – Ensures even pressure for effective cleaning.

## Subsystem 3: Navigation and Edge Detection

**Function:** Prevents the device from falling off the board by detecting edges and obstacles.

**Components:**
- **Microswitch Lever Arms** – Detect board edges and trigger direction changes.
- **ESP32 Microcontroller** – Processes sensor inputs and controls movement.
- **IMU Sensor (Optional, e.g., MPU-6050)** – Provides additional orientation data.

## Subsystem 4: Power Management

**Function:** Supplies stable power to all components.

**Components:**
- **LiPo Battery Pack** – Rechargeable power source.
- **LDO Voltage Regulator (e.g., LM7805)** – Steps down battery voltage for ESP32 and other components.

## Subsystem 5: Enclosure

**Function:** Protects components and provides a lightweight, compact structure.

**Components:**
- **3D-Printed Housing** – Custom enclosure for durability and heat dissipation.

## Subsystem 6: PCB Design

**Function:** Integrates motor control, edge detection, and power management into a single, compact PCB.

**Components:**
- **Custom PCB** with footprints for:
- **ESP32**
- **Motor Driver ICs (TB6612FNG)**
- **Voltage Regulator (LM7805)**
- **Edge detection circuitry (microswitch connectors)**
- **Standard connectors for battery, motors, and sensors**

## Subsystem 7: Application

**Function:** Provides remote control, scheduling, and monitoring features.

**Features:**
- **Remote Activation** of the eraser.
- **Scheduled Cleaning Sessions** (e.g., set to clean at night or after class).
- **Manual Control** via app.
- **Usage History/Log** for tracking.
- **Status Monitoring** (Idle, Cleaning, Error).
- **Developed using Flutter or React Native** for cross-platform compatibility.

# Criterion for Success

BoardBuddy will be considered successful if it meets the following criteria:

- **Effective Erasing:** Cleans most of residue in a single pass.
- **Secure Mounting:** Neodymium magnets must hold the device firmly on the board without slipping.
- **Edge Detection:** The device must detect board edges and avoid falling off.
- **Smooth Locomotion:** Omnidirectional wheels must provide **consistent and unrestricted movement**.
- **Reliable Power:** The battery must provide **at least 30 minutes of continuous operation**.
- **Compact Design:** The device must be lightweight and compact to minimize magnet usage.
- **Custom PCB Functionality:** The PCB must integrate motor control, edge detection, and power management without external breadboards or components.
- **Application Integration:** The app must allow for **remote control, scheduling, and monitoring**.

CHARM: CHeap Accessible Resilient Mesh for Remote Locations and Disaster Relief

Martin Michalski, Melissa Pai, Trevor Wong

Featured Project

# CHARM: CHeap Accessible Resilient Mesh for Remote Locations and Disaster Relief

Team Members:

- Martin Michalski (martinm6)

- Trevor Wong (txwong2)

- Melissa Pai (mepai2)

# Problem

There are many situations in which it is difficult to access communicative networks. In disaster areas, internet connectivity is critical for communication and organization of rescue efforts. In remote areas, a single internet connection point often does not cover an area large enough to be of practical use for institutions such as schools and large businesses.

# Solution

To solve these problems, we would like to create a set of meshing, cheap, lightweight, and self-contained wireless access points, deployable via drone. After being placed by drone or administrator, these access points form a WiFi network, usable by rescuers, survivors, and civilians. Our network will have QoS features to prioritize network traffic originating from rescuers. Having nodes/access points deployable by drone ensures we are able to establish timely connectivity in areas where search and rescue operations are still unable to reach.

Over the course of the semester, we will produce a couple of prototypes of these network nodes, with built in power management and environmental sensing. We aim to demonstrate our limited network’s mesh capabilities by setting up a mock network on one of the campus quads, and connecting at various locations.

# Solution Components

## Router and Wireless Access Point

Wireless Access for users and traffic routing will be the responsibility of an Omega2 board, with onboard Mediatek MT7688 CPU. For increased signal strength, the board will connect to a RP-SMA antenna via U.FL connector.

The Omega2 will be running OpenWRT, an Linux-based OS for routing devices. We will develop processes for the Omega2 to support our desired QoS features.

## Battery Management System

This module is responsible for charging the lithium-ion battery and ensuring battery health. Specifically, we will ensure the battery management system has the following features:

- Short circuit and overcurrent protection

- Over- and under-voltage protection

- An ADC to provide battery status data to the microcontroller

- 3.3v voltage regulation for the microcontroller and other sensors

In addition to miscellaneous capacitors and resistors, we intend to use the following components to implement the battery management system:

- The MT2492 step-down converter will be used to step down the output voltage of the battery to 3.3 volts. Between the GPS and extra power the microcontroller might consume with an upgraded Wifi antenna, low-dropout regulators would not provide sufficient power in an efficient manner. Instead, we will implement a 2 amp buck converter to improve efficiency and ensure there are no current bottlenecks.

- We will utilize two button-top protected 18650 3400 mAh lithium ion batteries in series to power each node. Placing two of these batteries in series will ensure their combined voltage never falls below the minimum voltage input of the buck converter, and accounting for the buck converter’s inefficiency these batteries should give us about 21 Wh of capacity. The cells we plan on using include a Ricoh R5478N101CD protection IC that provides over-voltage, under-voltage, and over-current protection. Using a standard battery form factor will make them easy to replace in the future as needed.

- A USB-C port with two pulldown resistors will provide 5 volt charging input with up to 3 amps of current, depending on the charger.

- The MT3608 step-up converter will boost the input voltage from the usb-c port and feed it into the charging controller.

- The MCP73844 Charge Management Controller will be used to charge the batteries. This controller supports CC/CV charging and a configurable current limit for safe and effective battery charging.

- The TI ADS1115 ADC will be used for battery voltage monitoring. This chip is used in the official Omega2 expansion board, so it should be easy to integrate in software. We will use a voltage divider to reduce the battery voltage to a range this chip can measure, and this chip will communicate over an I2C bus.

## Sensor Suite

Each node will have a battery voltage sensor and GPS sensor, providing the system with health information for each node. On top of the Wifi-connectivity, each module would have a series of sensors to detect the status of the physical node and helpful environment variables. This sensor suit will have the following features and components to implement it

- Ultimate GPS Module PA1616D will be used for positioning information. This chip utilizes 3.3V which is supplied through our battery management system.

Battery Voltage Monitor

- The TI ADS1115 ADC (mentioned in the BMS section) is for battery voltage monitoring. It interfaces via I2C to the Omega2.

## System Monitor

A system monitor which provides visibility of the overall system status for deployed network nodes. Information that we will show includes: last known location, battery health, and network statistics (e.g. packets per second) from the physical devices.

We plan on using React to provide an intuitive UI, using google-map-react and other React packages to create an interactive map showing the last known location and status of each node.

The backend will be hosted on a server in the cloud. Nodes will continually update the server with their status via POST requests.

# Criterion For Success

We aim to achieve the following performance metrics:

- 1.5 kg maximum mass

- Cover 7500 m^2 (North Quad) with 4 nodes

- Display the last known location, time connected, and battery voltage for all nodes via our system monitor

- 3 hour battery life

- 5 Mb/s WiFi available to laptops and smartphones in the coverage area

[*Link*](https://courses.engr.illinois.edu/ece445/pace/view-topic.asp?id=71252) *to assciated WebBoard discussion*