Project

# Title Team Members TA Documents Sponsor
31 Resonant Cavity Field Profiler
Outstanding Engineering Award
Furkan Yazici
Max Goin
Salaj Ganesh
Stasiu Chyczewski design_document1.pdf
design_document2.pdf
final_paper1.pdf
photo1.JPEG
photo2.JPEG
presentation1.pdf
proposal1.pdf
video
# Team Members:
- Max Goin (jgoin2)
- Furkan Yazici (fyazici2)
- Salaj Ganesh (salajg2)

# Problem

We are interested in completing the project proposal submitted by Starfire for designing a device to tune Resonant Cavity Particle Accelerators. We are working with Tom Houlahan, the engineer responsible for the project, and have met with him to discuss the project already.

Resonant Cavity Particle Accelerators require fine control and characterization of their electric field to function correctly. This can be accomplished by pulling a metal bead through the cavities displacing empty volume occupied by the field, resulting in measurable changes to its operation. This is typically done manually, which is very time-consuming (can take up to 2 days).

# Solution

We intend on massively speeding up this process by designing an apparatus to automate the process using a microcontroller and stepper motor driver. This device will move the bead through all 4 cavities of the accelerator while simultaneously making measurements to estimate the current field conditions in response to the bead. This will help technicians properly tune the cavities to obtain optimum performance.

# Solution Components

## MCU:
STM32Fxxx (depending on availability)
Supplies drive signals to a stepper motor to step the metal bead through the 4 quadrants of the RF cavity. Controls a front panel to indicate the current state of the system. Communicates to an external computer to allow the user to set operating conditions and to log position and field intensity data for further analysis.
An MCU with a decent onboard ADC and DAC would be preferred to keep design complexity minimum. Otherwise, high MIPS performance isn’t critical.

## Frequency-Lock Circuitry:
Maintains a drive frequency that is equal to the resonant frequency. A series of op-amps will filter and form a control loop from output signals from the RF front end before sampling by the ADCs. 2 Op-Amps will be required for this task with no specific performance requirements.

## AC/DC Conversion & Regulation:
Takes an AC voltage(120V, 60Hz) from the wall and supplies a stable DC voltage to power MCU and motor driver. Ripple output must meet minimum specifications as stated in the selected MCU datasheet.

## Stepper Drive:
IC to control a stepper motor. There are many options available, for example, a Trinamic TMC2100. Any stepper driver with a decent resolution will work just fine. The stepper motor will not experience large loading, so the part choice can be very flexible.

## ADC/DAC:
Samples feedback signals from the RF front end and outputs the digital signal to MCU. This component may also be built into the MCU.

## Front Panel Indicator:
Displays the system's current state, most likely a couple of LEDs indicating progress/completion of tuning.

## USB Interface:
Establishes communication between the MCU and computer. This component may also be built into the MCU.

## Software:
Logs the data gathered by the MCU for future use over the USB connection. The position of the metal ball and phase shift will be recorded for analysis.

## Test Bed:
We will have a small (~ 1 foot) proof of concept accelerator for the purposes of testing. It will be supplied by Starfire with the required hardware for testing. This can be left in the lab for us to use as needed. The final demonstration will be with a full-size accelerator.

# Criterion For Success:

- Demonstrate successful field characterization within the resonant cavities on a full-sized accelerator.
- Data will be logged on a PC for later use.
- Characterization completion will be faster than current methods.
- The device would not need any input from an operator until completion.

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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