# Title Team Members TA Documents Sponsor
1 CHARM: CHeap Accessible Resilient Mesh for Remote Locations and Disaster Relief
Martin Michalski
Melissa Pai
Trevor Wong
Jeff Chang design_document1.pdf
# 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*]( *to assciated WebBoard discussion*

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.

Project Videos