| Issue |
E3S Web Conf.
Volume 387, 2023
International Conference on Smart Engineering for Renewable Energy Technologies (ICSERET-2023)
|
|
|---|---|---|
| Article Number | 05005 | |
| Number of page(s) | 8 | |
| Section | Information Secutity | |
| DOI | https://doi.org/10.1051/e3sconf/202338705005 | |
| Published online | 15 May 2023 | |
Design and Development of a Solar- Powered UAV Using IoT and Machine Learning
1 Prince Shri Bhavani College Of Engineering and Technology, Approved by AICTE, Affiliated To Anna University, India
2 Department of Information technology, M. Kumarasamy college of engineering, Karur - 639113
3 Assistant Professor, Prince Dr. K. Vasudevan College of Engineering and Technology, Chennai - 127
4 Assistant Professor, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127
* Correspondingauthor: srijan.it@mkce.ac.in
The proposed solar-powered UAV utilizes photovoltaic panels to convert solar energy into electrical power to supply the onboard electronic systems, including the propulsion system and sensors. To enhance the UAV's performance, loT technology is employed to enable the communication and coordination of the UAV with other connected devices, such as ground stations and sensors. This allows for real-time data collection and analysis, as well as improved situational awareness for the UAV. To optimize the performance of the UAV, SVM is used as a machine learning algorithm for object detection and classification. SVM has been widely used in UAVs to detect and classify objects in aerial images, and has been proven to be effective in a variety of applications. The proposed design utilizes SVM to detect and classify objects of interest, such as crops, buildings, and infrastructure, and to assist in the navigation and control of the UAV. The design and development of the proposed solar-powered UAV involves several key components, including the solar panels, propulsion system, control system, and communication system.
Key words: Design / development / solar-powered UAV / Internet of Things / IoT sensors / Machine Learning / ML algorithms
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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