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Open Access
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Abhishek S., Anirudh K.K, Gautham G. Lal, Faris Muhammed,
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- Student,, Student,, Student,, Student, Department of Electrical and Electronics Engineering, TKM College of Engineering,, Department of Electrical and Electronics Engineering, TKM College of Engineering,, Department of Electrical and Electronics Engineering, TKM College of Engineering,, Department of Electrical and Electronics Engineering, TKM College of Engineering, Kerala,, Kerala,, Kerala,, Kerala, India, India, India, India
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Abstract
nUnmanned Aerial Vehicles (UAVs), commonly known as drones, have become integral across diverse fields such as agriculture, surveillance, and defense, with expanding roles in critical operations like search and rescue and post-disaster management. Despite their versatility, current UAVs encounter challenges in disaster response due to limitations in flight time, costs, and accuracy, particularly in dynamic weather conditions. The UAV is equipped with features essential for disaster site surveillance, human detection, communication with isolated individuals, and the delivery of vital supplies such as medical aid or food packages. The hardware foundation comprises a standard F450 drone frame and an APM flight controller. For human detection, a cost-effective solution utilizing a pre-trained machine learning model, YOLOv8, in conjunction with an FPV camera is implemented. Additional sensors can be seamlessly integrated to enhance human detection capabilities. Computational tasks are managed through a ground control station, ensuring the efficient execution of algorithms. Communication with affected individuals is facilitated by a SIM800L GSM GPRS module, accompanied by dedicated microphone and speaker components. Future developments include incorporating a modular dropping mechanism to the drone for flexible deployment options. The prototype aims for semi-autonomous control, detecting humans at altitudes of 10-25 meters and facilitating communication or dropping packages at specified locations. As a future scope, a hybrid UAV with rover capabilities can further enhance operational efficiency. The successful implementation of this drone prototype promises significant advancements in disaster response capabilities, leading to improved situational awareness, efficient communication, and timely delivery of essential supplies to affected areas.
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Keywords: YOLO, UAV, Mission Planner, Machine learning, Human detection
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Aerospace Engineering & Technology(joaet)]
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References
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| Volume | 14 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | May 20, 2024 | |
| Accepted | June 4, 2024 | |
| Published | July 22, 2024 |
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