Deep Learning Approach for Unmanned Aerial Vehicle Landing
Utkarsh R. Moholkar1, Dipti D. Patil2, Vinod Kumar3, Archana Patil4
1Utkarsh R. Moholkar, Research Scholar, Artificial Intelligence & Robotics, College of Engineering, Pune (Maharashtra), India.
2Dipti D. Patil, Associate Professor, Department of Information Technology, MKSSS’s Cummins College of Engineering for Women, Pune (Maharashtra), India.
3Vinod Kumar, Director, U.R. Rao Satellite Centre, Indian Space Research Organization, Bengaluru (Karnataka), India.
4Archana Patil, Assistant Professor, Department of Computer Engineering & Information Technology, College of Engineering, Pune (Maharashtra), India.
Manuscript received on 16 August 2022 | Revised Manuscript received on 22 August 2022 | Manuscript Accepted on 15 September 2022 | Manuscript published on 30 September 2022 | PP: 1-4 | Volume-11 Issue-10, September 2022 | Retrieval Number: 100.1/ijitee.J926309111022 | DOI: 10.35940/ijitee.J9263.09111022
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: It is one of the biggest challenges to land an unmanned aerial vehicle (UAV). Landing it by making its own decisions is almost impossible even if progress has been made in developing deep learning algorithms, which are doing a great job in the Artificial Intelligence sector. But these algorithms require a large amount of data to get optimum results. For a Type-I civilization collecting data while landing UAV on another planet is not feasible. But there is one hack all the required data can be collected by creating a simulation that is cost-effective, time-saving, and safe too. This is a small step toward making an Intelligent UAV that can make its own decisions while landing on a surface other than Earth’s surface. Therefore, the simulation has been created inside gaming engine from which the required training data can be collected. And by using that training data, deep neural networks are trained. Also deployed those trained models into the simulation and checked their performance.
Keywords: Artificial intelligence, Deep learning, Unmanned Aerial Vehicle.
Scope of the Article: Deep learning