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Avian-inspired feature-based relative positioning strategy for formation control of multiple unmanned aerial vehicles


Mohamed Yaghoobi, Yousef Jassim Ismail Abdulla (2019) Avian-inspired feature-based relative positioning strategy for formation control of multiple unmanned aerial vehicles. Masters thesis, Universiti Putra Malaysia.


As a new era of flying machines intersected autonomous and semi-autonomous machines, resulted in the birth of Unmanned Aerial Vehicles (UAVs). It has changed the way human beings travel, transport objects, surveillance execution, emergency response, and other things which time will reveal. Among the various types of UAVs, the Multi-rotor UAVs attained the most attention due to its advantages such as: ease of use, Vertical Take Off and Landing capability, hover flight, and ability to operate in confined area. However, small payload capacity is one of the most discerning disadvantage. Due to limited capability and performance of single Multi-rotor UAVs, interest to overcome this through flight formation and formation control of UAVs has grown significantly over the past few years. One of the key aspects of flight formation is spatial coordination or relative positioning between UAVs flying in close proximity in order to avoid collision and achieve collective operation. In spatial coordination of UAVs flying in the swarm, using the vision-based technique for on-board computation, there have been two main approaches, which are; Color- based (artificial marker detection) and Motion-based (Optical Flow). The Color-based approach performance is highly affected by misdetection for indoor application and light intensity variation for outdoor application. The Motion-based or Optical Flow approach for both indoor and outdoor application suffers from lack of precision and high sensitivity to noise. To the best of our knowledge at the time of writing this thesis, there are nearly no studies which focused on the use of feature-detection approach in real-time and on-board of UAVs for the collision-free flight formation. As inspired by birds flying in flocks, vision is one of the most critical component for them to be able to respond to their neighbor’s motion. Thus, in this thesis a novel approach in developing a Vision System as a primary sensor for relative positioning in flight formation of Leader-Follower scenario is introduced. The developed Vision System is based on Feature-Detection and stereo vision. It utilizes the On-Line Machine Learning approach for tracking the Leader in Leader-Follower flight formation. The NVIDIA JETSON TX1 is used as a computing platform for processing the Vision System data in real-time and on-board of DJI MATRICE 100 quadcopter. In order to evaluate the Vision System performance, three flight-formation scenarios which are tracking the Leader in motion, following the Leader in motion and tracking the Leader in presence of obstacle were introduced. The test results from the first flight-formation show the 99% success in tracking the Leader in motion when approximately 3600 training sample photo of the leader provided and 83% accuracy in calculating the location of the Leader. The second flight-formation test results show the ability of the Follower to follow the Leader with roughly 2 seconds delay by utilizing the Vision System. The results of third flight-formation show 85% success in tracking the Leader with 30% occlusion, 75% success in case of 50% occlusion and poor performance in case of 100% occlusion. The obtained test results show the developed Vision System which is based on Feature-Detection to be a better alternative to the existing approaches which are Color-based and Motion-based.

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Additional Metadata

Item Type: Thesis (Masters)
Subject: Drone aircraft - Control systems
Subject: Drone aircraft - Computer networks
Call Number: FK 2019 128
Chairman Supervisor: Syaril Azrad Md. Ali, PhD
Divisions: Faculty of Engineering
Depositing User: Ms. Nur Faseha Mohd Kadim
Date Deposited: 26 Jan 2021 12:21
Last Modified: 04 Jan 2022 00:57
URI: http://psasir.upm.edu.my/id/eprint/84372
Statistic Details: View Download Statistic

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