PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Simulation and Real-Life Implementation of UAV Autonomous Landing System Based on Object Recognition and Tracking for Safe Landing in Uncertain Environments
Version 1
: Received: 12 January 2024 / Approved: 12 January 2024 / Online: 12 January 2024 (15:10:20 CET)
How to cite:
Baidya, R.; Jeong, H. Simulation and Real-Life Implementation of UAV Autonomous Landing System Based on Object Recognition and Tracking for Safe Landing in Uncertain Environments. Preprints2024, 2024011037. https://doi.org/10.20944/preprints202401.1037.v1
Baidya, R.; Jeong, H. Simulation and Real-Life Implementation of UAV Autonomous Landing System Based on Object Recognition and Tracking for Safe Landing in Uncertain Environments. Preprints 2024, 2024011037. https://doi.org/10.20944/preprints202401.1037.v1
Baidya, R.; Jeong, H. Simulation and Real-Life Implementation of UAV Autonomous Landing System Based on Object Recognition and Tracking for Safe Landing in Uncertain Environments. Preprints2024, 2024011037. https://doi.org/10.20944/preprints202401.1037.v1
APA Style
Baidya, R., & Jeong, H. (2024). Simulation and Real-Life Implementation of UAV Autonomous Landing System Based on Object Recognition and Tracking for Safe Landing in Uncertain Environments. Preprints. https://doi.org/10.20944/preprints202401.1037.v1
Chicago/Turabian Style
Baidya, R. and Heon Jeong. 2024 "Simulation and Real-Life Implementation of UAV Autonomous Landing System Based on Object Recognition and Tracking for Safe Landing in Uncertain Environments" Preprints. https://doi.org/10.20944/preprints202401.1037.v1
Abstract
The use of autonomous Unmanned Aerial Vehicles (UAVs) has been increasing, and the autonomy of these systems and their capabilities in dealing with uncertainties is crucial. Autonomous landing is pivotal for the success of an autonomous mission of UAVs. This paper presents an autonomous landing system for quadrotor UAVs with the ability to perform smooth landing even in undesirable conditions like obstruction by obstacles in and around the designated landing area and inability to identify or the absence of a visual marker establishing the designated landing area. We have integrated algorithms like version 5 of You Only Look Once (YOLOv5), DeepSORT, Euclidean distance transform, and Proportional-Integral-Derivative (PID) controller to strengthen the robustness of the overall system. While the YOLOv5 model is trained to identify the visual marker of the landing area and some common obstacles like people, cars, and trees, the DeepSORT algorithm keeps track of the identified objects. Similarly, using the detection of the identified objects and Euclidean distance transform, an open space without any obstacles to land could be identified if necessary. Finally, the PID controller generates appropriate movement values for the UAV using the visual cues of the target landing area and the obstacles. To warrant the validity of the overall system without risking the safety of the involved people, initial tests are performed, and a software-based simulation is performed before executing the tests in real life. A full-blown hardware system with an autonomous landing system is then built and tested in real life. The designed system is tested in various scenarios to verify the effectiveness of the system.
Keywords
Intelligent autonomous system; Autonomous Landing; Obstacle avoidance; Object detection; Quadrotors; Deep SORT; YOLOv5; Distance Transform; PID control
Subject
Computer Science and Mathematics, Robotics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
