Rapid Response UAV network for Disaster Relief and Survivor Location using Machine Learning and GPS
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SANTA CLARA, Calif. - Californer -- Background:

Disasters, whether natural or man-made, can have devastating impacts on communities, resulting in loss of life, injuries, and widespread damage. In the aftermath of such events, rapid response and effective search and rescue operations are crucial to saving lives and providing necessary relief to affected individuals. Traditional methods of locating survivors often involve extensive ground searches, which can be time-consuming and inefficient, particularly in large or difficult-to-access areas.

In recent years, advancements in unmanned aerial vehicle (UAV) technology have opened up new possibilities for enhancing disaster response efforts. UAVs equipped with advanced cameras, sensors, ML-systems, real-time communication capabilities, and autonomous navigation systems can significantly improve the speed and accuracy of locating individuals in distress. By leveraging aerial views, these UAVs can cover vast areas quickly, identifying survivors, assessing damage, and guiding emergency response teams to critical locations.

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However, the effective deployment of UAVs in disaster scenarios requires careful consideration of various factors, including environmental challenges, regulatory compliance, and integration with existing emergency response frameworks. As such, there is a pressing need for a dedicated project focused on creating a UAV specifically designed to address these challenges.

Purpose:

The goal of this research is to make it easier to locate people in a disaster-affected area. Responders will be able to provide care more quickly as a result, and the drone will be able to provide a limited quantity. Machine learning will be used in our project to recognize objects using infrared and audio signals.

Our drone uses an infrared camera to detect humans at night, allowing detection even if it is pitch black. On top of this, our project innovates by using machine learning on the camera feed, specifically object detection. The feed is sent back to the ground station computer running the model, allowing for fast and accurate detection. Our project also utilizes machine learning in the form of audio. Audio feed will be transmitted over radio to the computer, running another model in tandem that is able to detect human voices. This allows for an even tighter sweep for signs of humans. The project runs using GPS location to be able to accurately pinpoint the location of any humans. Our project will incorporate a bracelet that sends GPS signals for drones to help locate and find.

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Currently, our team is looking for supporters. You can support us by going to the forum below and liking our post. https://tinyurl.com/rapidresponseuav

Our funding campaign will also start soon. Please bookmark this page and ask questions in the discussion if you are interesting in funding. https://experiment.com/projects/rapid-response-uav-post-disaster-location-network-incorporating-ml-radio-control-and-global-positioning-system

Contact
STEM Leadership Science Fair Team
***@scusd.net


Source: STEM Leadership Institute Science Fair Team
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