## Inspiration

We are tinkerers and builders who love getting our hands on new technologies. When we discovered that the Spot Robot Dog from Boston Dynamics was available to build our project upon, we devised different ideas about the real-world benefits of robot dogs. From a conversational companion to a navigational assistant, we bounced off different ideas and ultimately decided to use the Spot robot to detect explosives in the surrounding environment as we realized the immense amount of time and resources that are put into training real dogs to perform these dangerous yet important tasks.

## What it does

Lucy uses the capabilities of Spot Robot Dog to help identify potentially threatening elements in a surrounding through computer vision and advanced wave sensing capabilities. A user can command the dog to inspect a certain geographic area and the dog autonomously walks around the entire area and flags objects that could be a potential threat. It captures both raw and thermal images of the given object in multiple frames, which are then stored on a vector database and can be searched through semantic search.

This project is a simplified approach inspired by the research "Atomic Magnetometer Multisensor Array for rf Interference Mitigation and Unshielded Detection of Nuclear Quadrupole Resonance" (<https://link.aps.org/accepted/10.1103/PhysRevApplied.6.064014>).

## How we built it

We've combined the capabilities of OpenCV with a thermal sensing camera to allow Spot Robot to identify and flag potentially threatening elements in a given surrounding. To simulate these elements in the surroundings, we built a simple Arduino application that emits light waves in irregular patterns. The robot dog operates independently through speech instructions, which are powered by DeepGram's Speech to Text and Llama-3-8b model hosted on the Groq platform. Furthermore, we've leveraged ChromDB's vector database to tokenize images that allow people to easily search through images, which are captured in the range of 20-40fps.

## Challenges we ran into

The biggest challenge we encountered was executing and testing our code on Spot due to the unreliable internet connection. We also faced configuration issues, as some parts of modules were not supported and used an older version, leading to multiple errors during testing. Additionally, the limited space made it difficult to effectively run and test the code.

## Accomplishments that we're proud of

We are proud that we took on the challenge of working with something that we had never worked with before and even after many hiccups and obstacles we were able to convert our idea in our brains into a physical reality.

## What we learned

We learned how to integrate and deploy our program onto Spot. We also learned that to work around the limitations of the technology and our experience working with them.

## What's next for Lucy

We want to integrate LiDar in our approach, providing more accurate results then cameras. We plan to experiment beyond light to include different wave forms, thus helping improve the reliability of the results.
## What it does

SPOT would be equipped with various sensors, cameras, and LIDAR technology to perform inspections in hazardous environments. A network of SPOT bots will be deployed in a within a 2.5 – 3-mile radius surrounding a particular infrastructure for security and surveillance tasks, patrolling areas and providing real-time video feeds to human operators. It can be used to monitor large facilities, industrial sites, or public events, enhancing security effort.

These network of SPOT robots will be used to inspect and collect data/mages for analysis, tracking suspects, and gathering crucial intelligence in high-risk environments thus maintaining situational awareness without putting officers in harm's way.

They will be providing real-time video feeds . If it detects any malicious activity, the SPOT will act as the first respondent and deploy non-lethal measures by sending a distress signal to the closest law enforcement officer/authority who’d be able mitigate the situation effectively. Consequently, the other SPOT bots in the network would also be alerted. Its ability to provide real-time situational awareness without putting officers at risk is a significant advantage.

## How we built it

* Together.ai : Used llama to enable conversations and consensus among agents
* MindsDB : Database is stored in postgres (render). The database is imported to Mindsdb. The sentiment classifier is trained with the help of demo data and the sentiments which are retrieved from every agent allows us to understand the mental state of every bot
* Reflex : UI for visualization of statistical measures of the bots
-Intel : To train mobilevnet for classifying threats
* Intersystems : To Carry on Battery Life forecasting for the agent to enable efficient decisions
## Inspiration

There are two types of pets wandering unsupervised in the streets - ones that are lost and ones that don't have a home to go to. Pet's Palace portable mini-shelters services these animals and connects them to necessary services while leveraging the power of IoT.

## What it does

The units are placed in streets and alleyways. As an animal approaches the unit, an ultrasonic sensor triggers the door to open and dispenses a pellet of food. Once inside, a live stream of the interior of the unit is sent to local animal shelters which they can then analyze and dispatch representatives accordingly. Backend analysis of the footage provides information on breed identification, custom trained data, and uploads an image to a lost and found database. A chatbot is implemented between the unit and a responder at the animal shelter.

## How we built it

Several Arduino microcontrollers distribute the hardware tasks within the unit with the aide of a wifi chip coded in Python. IBM Watson powers machine learning analysis of the video content generated in the interior of the unit. The adoption agency views the live stream and related data from a web interface coded with Javascript.

## Challenges we ran into

Integrating the various technologies/endpoints with one Firebase backend.

## Accomplishments that we're proud of

A fully functional prototype!