Project

# Title Team Members TA Documents Sponsor
43 Kitchen Dry Ingredient Tracker
Anju Jain
Nynika Badam
Sanjana Kumar
Vishal Dayalan design_document1.pdf
final_paper1.pdf
photo1.jpg
photo2.heic
photo3.heic
presentation1.pdf
proposal1.pdf
video
**Kitchen Dry Ingredient Tracker**

Team Members:
- Anju Jain (anjuyj2)
- Nynika Badam (nbadam2)
- Sanjana Kumar (spkumar4)

**Problem**

In our day to day lives, it's hard to keep track of ingredients in our kitchen and make sure we replenish it often. In order to remedy this, we propose a kitchen dry ingredient tracker.

**Solution**

Our system is designed to track and communicate with users about their ingredient necessities. Each individual ingredient tracker can be tailored to different lower weight threshold measurements.
Our system will use an app to maintain a digital grocery list. If an ingredient is running low, our system will add the ingredient to a digital grocery list. We also will have the option of adding the ingredient to the user's choice of online shopping cart. Users can remove ingredients' names from the list after purchase. ​​If a user is outside and is close to a grocery store (500 m), mobile app notification will be sent to the user's phone to notify them about necessary ingredient/s.

**Solution Components**

## Subsystem 1: LED
LED lights are placed at each ingredient and will light up when a certain percentage of total ingredients are low to indicate a more urgent grocery run.
Components: LEDs (from previous semester lab kits) or LED strip (12V-NB-CW-01M), LED Driver

## Subsystem 2: Weight Sensor
Our system will have 3 weight sensors to track 3 different ingredients. This can be extended for a system with more ingredients.
Each weight sensor will have a button to indicate if that weight sensor is active.
The weight sensor will be used to make sure the dry ingredient has not gone below the minimum weight limit.
Components: weight sensor Alpha (Taiwan) MF01A-N-221-A05, button (from previous lab kits)

## Subsystem 3: Microcontroller
Our system will be powered by plugging the microcontroller to the wall.
It will keep constant track of weight fluctuations for ingredients and send the data to the app.
It will be responsible for controlling individual ingredient’s LEDs.
Components: Microcontroller

## Subsystem 4: App
We will build an Apple based mobile app to provide connectivity between the user and the system.
User specifies which weight sensor station corresponds to what ingredient and its lower weight threshold (grams).
The app will maintain a digital grocery list.
If an ingredient is running low, our system will add the ingredient to a digital grocery list.
We also will have the option of adding the ingredient to the user's choice of online shopping cart.
Users can remove ingredients' names from the list after purchase.
​​If a user is outside and is close to a grocery store (500 m), mobile app notification will be sent to the user's phone to notify them about necessary ingredient/s.

# Criterion For Success
1. System should be able to measure changes in ingredient weights
- Add/Remove ingredient from grocery list/ online store shopping cart
2. Indicate when an ingredient needs replenishing through app
- mobile app should add ingredient name to digital shopping list
- Or add ingredient to an online store shopping cart
3. When many ingredients (2 out of 3) are low, LED lights should turn on around these ingredients
4. If the user’s phone is 500 m or less from a grocery store, mobile app should send reminder to visit the store if there are ingredients in the digital grocery list (if the user chose not to go the online shopping route)

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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