## Inspiration

We were very inspired by the chaotic innovations happening all around the world and wanted to create something that's just a little stupid, silly, and fun using components that are way too overspecced for the usecase.

## What it does

It is intended to use electromagnetic properties and an intense amount of power to fuel a magnetic accelerator that will accelerate objects towards some destination. This was then switched to a coil gun model, and then using a targeting system made up of a turret and a TFLite model we aim for stupid objects with the power of AI.

## How we built it

We attempted to create an electromagnet and fiddled around for a while with the Qualcomm HDK

## Challenges we ran into

We severely underestimated how short 24 hours is and a lot of the components intended to be added onto the creation do not function as intended.

## Accomplishments that we're proud of

We mixed a huge aspect of electrophysics with modern day cutting edge engineering to create something unique and fun!

## What we learned

Time is short, and this is definitely a project we want to continue into the future.

## What's next for Magnetic Accelerator

To develop it properly and aim properly in the future (summer 2023)
## Inspiration

Driven by the harrowing reality that civilians comprised a staggering 95% of all cluster munition casualties in 2022, we were impassioned to devise a solution that could confront this pressing humanitarian crisis head-on. With up to 40% of these munitions failing to explode upon impact, the threat to civilian lives persists long after conflict ceases. In regions like Laos, where between 1964 and 1973, 260 million cluster bomblets were dropped, and a chilling 80 million failed to detonate, the urgency of our mission became abundantly clear. Inspired by the imperative to safeguard innocent lives in post-war zones, we embarked on the development of Project Horus.

## What it does

Project Horus utilizes cutting-edge technology to detect and localize unexploded cluster munitions in conflict-affected areas. Using a Parrot drone equipped with a custom-trained Convolutional Neural Network (CNN), our system autonomously scans vast territories, identifying potential threats. The drone's onboard Inertial Measurement Unit (IMU) aids in precisely localizing the detected munitions, even in electronic warfare or GPS-denied environments. Results are visualized in real-time as a heatmap, providing actionable insights to demining teams and enabling targeted removal of these deadly remnants of war.

## How we built it

We started by creating a robust training dataset by printing out images of cluster munitions, placing them on the ground, and capturing a video to partition into frames. Our CNN, built using TensorFlow, achieved an impressive 98% validation accuracy after rigorous training. Integration with the Parrot drone involved deploying grid search algorithms to autonomously search for munitions, leveraging the drone's capabilities for real-time bomb detection. Overcoming challenges such as uploading custom assets into the simulator and optimizing the model to run efficiently on non-hardware accelerated systems, we created a streamlined solution ready for deployment in the field.

## Challenges we ran into

Throughout the development process, we encountered several challenges, including difficulties uploading custom assets into the simulator and optimizing the CNN to run efficiently on hardware-constrained systems. Despite these hurdles, we adapted our approach, moving the computer vision processing off the drone and onto a separate system, ensuring our solution remained viable and effective.

## Accomplishments that we're proud of

We're proud to have successfully implemented grid search and bomb-detection capabilities on the Parrot drone, as well as leveraging the IMU for precise localization of munitions. Additionally, visualizing the detection results in a heatmap provides actionable intelligence for demining teams, marking a significant achievement in our mission to save civilian lives.

## What's next for Project Horus

Moving forward, we aim to enhance the robustness of our model by incorporating synthetic and real-world data for fine-tuning. Furthermore, we envision implementing multi-agent reinforcement learning techniques to enable collaborative scanning by fleets of drones, enhancing efficiency in covering large areas. Ultimately, we aspire to deploy Project Horus alongside demining teams in regions such as Ukraine, Israel, Afghanistan, and Southeast Asia, contributing to the safe removal of cluster munitions and the protection of civilian populations.
## Inspiration

Minecraft has an interesting map mechanic where your character holds a map which "draws itself" while exploring the world. I am also very interested in building a plotter, which is a printer that uses a pen and (XY) gantry to produce images. These ideas seemed to fit together quite well.

## What it does

Press a button, copy GPS coordinates and run the custom "gcode" compiler to generate machine/motor driving code for the arduino. Wait around 15 minutes for a 48 x 48 output.

## How we built it

Mechanical assembly - Tore apart 3 dvd drives and extracted a multitude of components, including sled motors (linear rails). Unfortunately, they used limit switch + DC motor rather than stepper, so I had to saw apart the enclosure and **glue** in my own steppers with a gear which (you guessed it) was also glued to the motor shaft.

Electronics - I designed a simple algorithm to walk through an image matrix and translate it into motor code, that looks a lot like a video game control. Indeed, the stepperboi/autostepperboi main source code has utilities to manually control all three axes like a tiny claw machine :)

U - Pen Up
D - Pen Down
L - Pen Left
R - Pen Right
Y/T - Pen Forward (top)
B - Pen Backwards (bottom)
Z - zero the calibration
O - returned to previous zeroed position

## Challenges we ran into

* I have no idea about basic mechanics / manufacturing so it's pretty slipshod, the fractional resolution I managed to extract is impressive in its own right
* Designing my own 'gcode' simplification was a little complicated, and produces strange, pointillist results. I like it though.

## Accomplishments that we're proud of

* 24 hours and a pretty small cost in parts to make a functioning plotter!
* Connected to mapbox api and did image processing quite successfully, including machine code generation / interpretation

## What we learned

* You don't need to take MIE243 to do low precision work, all you need is superglue, a glue gun and a dream
* GPS modules are finnicky and need to be somewhat near to a window with built in antenna
* Vectorizing an image is quite a complex problem
* Mechanical engineering is difficult
* Steppers are *extremely* precise, and I am quite surprised at the output quality given that it's barely held together.
* Iteration for mechanical structure is possible, but difficult
* How to use rotary tool and not amputate fingers
* How to remove superglue from skin (lol)

## What's next for Cartoboy

* Compacting the design it so it can fit in a smaller profile, and work more like a polaroid camera as intended. (Maybe I will learn solidworks one of these days)
* Improving the gcode algorithm / tapping into existing gcode standard