## Inspiration

Inspired by the vision of Eve from WALL-E, EVE is our answer to a world demanding sustainable spaces. We imagined a robot that could meticulously assess building health, just as Eve analyzed Earth's viability.

## What it does

Eve is a cutting-edge robot that assesses your building's environmental performance based off of [LEED](https://www.usgbc.org/leed) , [BREEAM](https://breeam.com/) , and [ISO 14001](https://www.iso.org/standard/60857.html) standards. The robot enabled with IR-motion detection navigates throughout your urban-building and captures image data which is processed using generative-ai to provide a comprehensive report of your building's sustainability.

## How we built it

We started with a LEGO Mindstorms EV3 robot, interfacing it with an NVIDIA Jetson for advanced computational capabilities. To enable autonomous navigation, we integrated a Luxonis AI camera that captures high-definition images, which are then sent to a hub using async sockets for fast real-time data transmission. These images are analyzed using Google Gemini to generate a comprehensive environmental report. Throughout the process, we focused on developing robust concurrency, efficient socket communication, and precise localization and mapping techniques.

## Challenges we ran into

Building EVE came with its set of challenges. Integrating different hardware components and ensuring seamless communication between them required extensive debugging. Implementing real-time data processing while maintaining accuracy was another hurdle. Additionally, perfecting the robot's navigation and localization within various building environments demanded meticulous calibration and algorithm refinement. We also planned to integrate audio and speech onto the robot but the speaker/microphone turned out to be incompatible with our microcontroller.

## Accomplishments that we're proud of

We successfully created a fully autonomous robot capable of navigating complex indoor environments while collecting and analyzing environmental data in real time. Our integration of high-definition imaging and advanced AI for environmental assessment sets EVE apart. The project's ability to generate a comprehensive report like LEED, BREEAM, and ISO 14001 is a significant achievement that we're extremely proud of.

## What we learned

Throughout this project, we gained valuable insights into the intricacies of hardware-software integration, particularly in the realms of concurrency and real-time data processing. We enhanced our understanding of socket programming, which was crucial for efficient data transmission. Our experience with localization and mapping provided a deeper appreciation for the complexities of autonomous navigation in dynamic environments.

## What's next for Eve

The future for EVE is bright. We plan to enhance its capabilities by incorporating additional sensors for more comprehensive environmental assessments. Improving the AI algorithms for faster and more accurate data analysis is another priority. We also envision expanding EVE's application scope beyond buildings to include outdoor environments and larger infrastructure projects and integrate speech and audio to enable actually talking to the robot. Ultimately, we aim to make EVE an indispensable tool in the quest for sustainable and healthy living spaces.
## Inspiration

We wanted to solve a unique problem we felt was impacting many people but was not receiving enough attention. With emerging and developing technology, we implemented neural network models to recognize objects and images, and converting them to an auditory output.

## What it does

XTS takes an **X** and turns it **T**o **S**peech.

## How we built it

We used PyTorch, Torchvision, and OpenCV using Python. This allowed us to utilize pre-trained convolutional neural network models and region-based convolutional neural network models without investing too much time into training an accurate model, as we had limited time to build this program.

## Challenges we ran into

While attempting to run the Python code, the video rendering and text-to-speech were out of sync and the frame-by-frame object recognition was limited in speed by our system's graphics processing and machine-learning model implementing capabilities. We also faced an issue while trying to use our computer's GPU for faster video rendering, which led to long periods of frustration trying to solve this issue due to backwards incompatibilities between module versions.

## Accomplishments that we're proud of

We are so proud that we were able to implement neural networks as well as implement object detection using Python. We were also happy to be able to test our program with various images and video recordings, and get an accurate output. Lastly we were able to create a sleek user-interface that would be able to integrate our program.

## What we learned

We learned how neural networks function and how to augment the machine learning model including dataset creation. We also learned object detection using Python.
## Inspiration

We wanted to create a robot that could navigate natural disaster zones and sort through the rubble and locate any human life.

## What it does

Our robot uses a passive infrared sensor to detect human heat in conjunction with an ultrasonic sensor which measures distance and helps accurately plot the shape of any room. It is controlled by a logitech joystick along with an ip camera from a remote location.

## How we built it

We built the base of the robot out of lego bricks in order to provide for a firm foundation that could handle the tremendous strain of the sensors. Then we used the ev3 brick to control two large servos that acted as the driving force behind our robot. We built an arm out of cardboard which was attached to the ev3 on one end and on the other end we attached the sensors along with a few small servos designed to rotate the sensors along the xy axis.

## Challenges we ran into

Some of the challenges we ran into were making the ardunio sensors functional as is the case usually. For an example, the PIR Sensor that we had was faulty and after a few hours of tinkering around we finally came to this realization when we borrowed a PIR sensor from the hardware lab.

## Accomplishments that we're proud of

Some accomplishments we are proud of is that fact that we were able to control the ev3 using a joystick as well as being able to use our phone as the "eyes" of the robot.

## What we learned

Along the way, we learned how to use the PIR sensor as difficult as it may have been along with how to stream data from our phone's camera to a remote computer.

## What's next for Detect&Save

What's next for Detect&Save is basically a lot of things. First of all, we need to get better sensors than the ones currently at our disposable since they are a serious limiting factor in terms of getting accurate and precise data. By upgrading the accuracy of our ultrasonic sensor for example we would be able to more successfully root out bad data points. Also, we need to acquire a very powerful motor than will be able to rotate the arm of the robot which contains all the sensors.