scholarly journals Drone-Monitoring: Improving the Detectability of Threatened Marine Megafauna

Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Jonathas Barreto ◽  
Luciano Cajaíba ◽  
João Batista Teixeira ◽  
Lorena Nascimento ◽  
Amanda Giacomo ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Behavioral Ecology ◽  
Brazilian Coast ◽  
Large Fish ◽  
Monitoring Method ◽  
Camera Angle ◽  
Aerial Vehicles ◽  
Sea Birds ◽  
Systematic Pattern ◽  
Marine Megafauna

Unmanned aerial vehicles (UAVs; or drones) are an emerging tool to provide a safer, cheaper, and quieter alternative to traditional methods of studying marine megafauna in a natural environment. The UFES Nectology Laboratory team developed a drone-monitoring to assess the impacts on megafauna related to the Fundão dam mining tailings disaster in the Southeast Brazilian coast. We have developed a systematic pattern to optimize the available resources by covering the largest possible area. The fauna observer can monitor the environment from a privileged angle with virtual reality and subsequently analyzes each video captured in 4k, allowing to deepening behavioral ecology knowledge. Applying the drone-monitoring method, we have observed an increasing detectability by adjusting the camera angle, height, orientation, and speed of the UAV; which saved time and resources for monitoring turtles, sea birds, large fish, and especially small cetaceans efficiently and comparably.

scholarly journals Automatic Object Detection, Labelling, and Localization by Camera’s Drone System

2021 ◽  
pp. 5008-5023
Author(s):  
Rasool D. Haameid ◽  
Bushra Q. Al-Abudi ◽  
Raaid N. Hassan
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Video Transmission ◽  
Low Cost ◽  
Base Station ◽  
Trigonometric Functions ◽  
One Dimensional ◽  
Camera Angle ◽  
Aerial Vehicles ◽  
Gps Devices ◽  
Objects Detection

This work explores the designing a system of an automated unmanned aerial vehicles (UAV( for objects detection, labelling, and localization using deep learning. This system takes pictures with a low-cost camera and uses a GPS unit to specify the positions. The data is sent to the base station via Wi-Fi connection. The proposed system consists of four main parts. First, the drone, which was assembled and installed, while a Raspberry Pi4 was added and the flight path was controlled. Second, various programs that were installed and downloaded to define the parts of the drone and its preparation for flight. In addition, this part included programs for both Raspberry Pi4 and servo, along with protocols for communication, video transmission, and sending and receiving signals between the drone and the computer. Third, a real-time, modified, one dimensional convolutional neural network (1D-CNN) algorithm, which was applied to detect and determine the type of the discovered objects (labelling). Fourth, GPS devices, which were used to determine the location of the drone starting and ending points . Trigonometric functions were then used for adjusting the camera angle and the drone altitude to calculate the direction of the detected object automatically. According to the performance evaluation conducted, the implemented system is capable of meeting the targeted requirements.

scholarly journals Autonomous Integrity Monitoring for Relative Navigation of Multiple Unmanned Aerial Vehicles

2021 ◽  
Vol 13 (8) ◽  
pp. 1483
Author(s):  
Yuan Sun
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Real Data ◽  
Wide Band ◽  
Relative Navigation ◽  
Monitoring Method ◽  
Integrity Monitoring ◽  
Position Information ◽  
Aerial Vehicles ◽  
Multiple Unmanned Aerial Vehicles ◽  
Multi Uav

Accurate and reliable relative navigation is the prerequisite to guarantee the effectiveness and safety of various multiple Unmanned Aerial Vehicles (UAVs) cooperation tasks, when absolute position information is unavailable or inaccurate. Among the UAV navigation techniques, Global Navigation Satellite System (GNSS) is widely used due to its worldwide coverage and simplicity in relative navigation. However, the observations of GNSS are vulnerable to different kinds of faults arising from transmission degradation, ionospheric scintillations, multipath, spoofing, and many other factors. In an effort to improve the reliability of multi-UAV relative navigation, an autonomous integrity monitoring method is proposed with a fusion of double differenced GNSS pseudoranges and Ultra Wide Band (UWB) ranging units. Specifically, the proposed method is designed to detect and exclude the fault observations effectively through a consistency check algorithm in the relative positioning system of the UAVs. Additionally, the protection level for multi-UAV relative navigation is estimated to evaluate whether the performance meets the formation flight and collision avoidance requirements. Simulated experiments derived from the real data are designed to verify the effectiveness of the proposed method in autonomous integrity monitoring for multi-UAV relative navigation.

Development of thickened semi-synthetic engine oil M-5z / 20 AERO for four-stroke gasoline engines aircraft piston of unmanned aerial vehicles (UAVs)

2020 ◽  
Vol 06 ◽  
pp. 44-47
Author(s):  
A.A. Moykin ◽  
◽  
A.S. Medzhibovsky ◽  
S.A. Kriushin ◽  
M.V. Seleznev ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Engine Oil ◽  
Motor Oil ◽  
Gasoline Engines ◽  
State Research Institute ◽  
Technical Requirements ◽  
Aerial Vehicles ◽  
State Research ◽  
Industrial Batch ◽  
The Russian Federation

Nowadays, the creation of remotely-piloted aerial vehicles for various purposes is regarded as one of the most relevant and promising trends of aircraft development. FAU "25 State Research Institute of Chemmotology of the Ministry of Defense of the Russian Federation" have studied the operation features of aircraft piston engines and developed technical requirements for motor oil for piston four-stroke UAV engines, as well as a new engine oil M-5z/20 AERO in cooperation with NPP KVALITET, LLC. Based on the complex of qualification tests, the stated operational properties of the experimental-industrial batch of M-5z/20 AERO oil are generally confirmed.

FEATURES OF ACOUSTIC NOISE OF SMALL UNMANNED AERIAL VEHICLES

2020 ◽  
Vol 79 (11) ◽  
pp. 985-995
Author(s):  
Valerii V. Semenets ◽  
V. M. Kartashov ◽  
V. I. Leonidov
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Acoustic Noise ◽  
Aerial Vehicles

PECULIARITIES OF SMALL UNMANNED AERIAL VEHICLES DETECTION AND RECOGNITION

2019 ◽  
Vol 78 (9) ◽  
pp. 771-781 ◽  
Author(s):  
V. M. Kartashov ◽  
V. N. Oleynikov ◽  
S. A. Sheyko ◽  
S. I. Babkin ◽  
I. V. Korytsev ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles ◽  
Detection And Recognition
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