Inspiration
Talking about the disaster prone areas, the major issues are the need of medication, and electricity. This becomes the more sever in a flooded area or where there is earthquake.
What it does
My solution involves a lot of features like -
- An app which can offer the shortest path on the map based on the different kind of calamities. This data is saved on google cloud and using google map API, we can use the data to alert the people.
- It is incorporated with ML and AI so that one can predict the extent.
- Also we have solar power based drones which can harvest the electricity and transmit it with the help of microwaves.
- The solar drone can also be used to provide medication to remote areas where it is not possible to reach. ## How we built it For the app,
- We have used python/Flask in the backend.
- Also for the drones, we are going to use raspberry pi, and train Open cv model to make the drone autonomous.
The solar drones are basically the drones having solar panels and run autonomously with the help of raspberry-pi and computer vision so that they can harvest the solar energy. Whenever there is a need, a person can raise a flag n the app, and the drone fetch the location and go there and transmit electricity via microwaves.
Challenges we ran into
The major challenge is to transmit the electricity wirelessly to the ground via microwaves.
Accomplishments that we're proud of
We have successfully trained the computer vision model for the autonomous drone so that we don't even need human intervention. The drone can itself identify the coordinates and the markers and deliver the parcel successfully on the simulation in Gazebo. Also, the app is in the process.
What we learned
I have learnt how to use ML/AI model for solving the issues. Also the use of pi in drone so as to transmit the power using the idea which is not yet in reality.
What's next for Lifelong
The next part is to train ML/AI model to predict the extent and to find the shortest and safest path to reach the disaster prone area.
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