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 Control System Architecture for Automatic Recovery of Fixed-Wing Unmanned Aerial Vehicles in a Moving Arrest System

2021 ◽  
Vol 103 (4) ◽  
Author(s):  
Kristoffer Gryte ◽  
Martin L. Sollie ◽  
Tor Arne Johansen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Low Cost ◽  
Field Testing ◽  
Satellite System ◽  
Base Station ◽  
Research Literature ◽  
Aerial Vehicles ◽  
Global Navigation Satellite ◽  
The Right ◽  
Automatic Recovery

AbstractAutomatic recovery is an important step in enabling fully autonomous missions using fixed-wing unmanned aerial vehicles (UAVs) operating from ships or other moving platforms. However, automatic recovery in moving arrest systems is only briefly studied in the research literature, and is not yet an option when using low-cost, commercial off-the-shelf (COTS) autopilots. Acknowledging the reliability and low cost of COTS avionics, this paper adds recovery functionality as a modular extension based on non-intrusive additions to an autopilot with very general assumptions on its interface. This is achieved by line-of-sight guidance, which sends an augmented desired position to the autopilot, to ensure line-following along a virtual runway that guides the UAV into the arrest system. The translation and rotation of this line is determined by the pose of the arrest system, determined using two Global Navigation Satellite System (GNSS) receivers, where one is configured as a Real-Time Kinematic (RTK) base station. The relative position of the UAV and arrest system is also precisely estimated using RTK GNSS. Through extensive field testing, on two different fixed-wing UAVs, the system has shown its performance and reliability; 43 recovery attempts in a stationary net hit 0.01 ± 0.25m to the right and 0.07 ± 0.20m below the target in calm conditions. Further, 15 recoveries in a barge-mounted, ship-towed net hit 0.06 ± 0.53m to the right and 0.98 ± 0.27m below the target in winds up to 4 m/s. The remaining error is largely systematic, caused by communication delays, and could be reduced with more integral effect or through direct compensation.

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 A Dynamic Topology Construction Algorithm for Self-Organizing Wireless Sensor Networks

2021 ◽  
Author(s):  
Waltenegus Dargie
Keyword(s):  
Wireless Sensor Networks ◽  
Environmental Factors ◽  
Unmanned Aerial Vehicles ◽  
Base Station ◽  
Wireless Sensor ◽  
Topology Construction ◽  
Construction Algorithm ◽  
Aerial Vehicles ◽  
Dynamic Topology ◽  
Self Organizing

<div>Self-organizing protocols and algorithms require knowledge of the underlying topology of the network. The topology can be represented by a graph or an adjacency matrix. In most practical cases, establishing the topology prior to a deployment is not possible because the exact placement of nodes and the existence of a reliable link between any two individual nodes cannot guaranteed. Therefore, this task has to be carried out after deployment. If the network is stand-alone and certain aspects are fixed (such as the identity of the base station, the size of the network, etc.), the task is achievable. If, however, the network has to interact with other systems -- such as Unmanned Aerial Vehicles (UAVs) or mobile robots -- whose operation is affected by environmental factors, the task can be difficult to achieve. In this paper we propose a dynamic topology construction algorithm, assuming that the network is a part of a joint deployment and does not have a fixed based station.</div>

scholarly journals Eyes in the Sky: Assessing the Feasibility of Low-Cost, Ready-to-Use Unmanned Aerial Vehicles to Monitor Primate Populations Directly

2019 ◽  
Vol 91 (1) ◽  
pp. 69-82
Author(s):  
Brandon P. Semel ◽  
Sarah M. Karpanty ◽  
Faramalala Francette Vololonirina ◽  
Ando Nantenaina Rakotonanahary
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Low Cost ◽  
Aerial Vehicles

scholarly journals Model-Based Autonomous Navigation with Moment of Inertia Estimation for Unmanned Aerial Vehicles

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2467 ◽  
Author(s):  
Hery Mwenegoha ◽  
Terry Moore ◽  
James Pinchin ◽  
Mark Jabbal
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Navigation System ◽  
Process Model ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Moment Of Inertia ◽  
Model Parameters ◽  
Main Process ◽  
Model Based ◽  
Aerial Vehicles

The dominant navigation system for low-cost, mass-market Unmanned Aerial Vehicles (UAVs) is based on an Inertial Navigation System (INS) coupled with a Global Navigation Satellite System (GNSS). However, problems tend to arise during periods of GNSS outage where the navigation solution degrades rapidly. Therefore, this paper details a model-based integration approach for fixed wing UAVs, using the Vehicle Dynamics Model (VDM) as the main process model aided by low-cost Micro-Electro-Mechanical Systems (MEMS) inertial sensors and GNSS measurements with moment of inertia calibration using an Unscented Kalman Filter (UKF). Results show that the position error does not exceed 14.5 m in all directions after 140 s of GNSS outage. Roll and pitch errors are bounded to 0.06 degrees and the error in yaw grows slowly to 0.65 degrees after 140 s of GNSS outage. The filter is able to estimate model parameters and even the moment of inertia terms even with significant coupling between them. Pitch and yaw moment coefficient terms present significant cross coupling while roll moment terms seem to be decorrelated from all of the other terms, whilst more dynamic manoeuvres could help to improve the overall observability of the parameters.

scholarly journals Accurate Landing of Unmanned Aerial Vehicles Using Ground Pattern Recognition

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1532 ◽  
Author(s):  
Jamie Wubben ◽  
Francisco Fabra ◽  
Carlos T. Calafate ◽  
Tomasz Krzeszowski ◽  
Johann M. Marquez-Barja ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Unmanned Aerial Vehicles ◽  
High Precision ◽  
Low Cost ◽  
Target Position ◽  
Flight Behavior ◽  
Ground Pattern ◽  
Aerial Vehicles ◽  
Exact Position ◽  
Precision Landing

Over the last few years, several researchers have been developing protocols and applications in order to autonomously land unmanned aerial vehicles (UAVs). However, most of the proposed protocols rely on expensive equipment or do not satisfy the high precision needs of some UAV applications such as package retrieval and delivery or the compact landing of UAV swarms. Therefore, in this work, a solution for high precision landing based on the use of ArUco markers is presented. In the proposed solution, a UAV equipped with a low-cost camera is able to detect ArUco markers sized 56 × 56 cm from an altitude of up to 30 m. Once the marker is detected, the UAV changes its flight behavior in order to land on the exact position where the marker is located. The proposal was evaluated and validated using both the ArduSim simulation platform and real UAV flights. The results show an average offset of only 11 cm from the target position, which vastly improves the landing accuracy compared to the traditional GPS-based landing, which typically deviates from the intended target by 1 to 3 m.

scholarly journals Operational Framework for Rapid, Very-high Resolution Mapping of Glacial Geomorphology Using Low-cost Unmanned Aerial Vehicles and Structure-from-Motion Approach

2019 ◽  
Vol 11 (1) ◽  
pp. 65 ◽  
Author(s):  
Marek W. Ewertowski ◽  
Aleksandra M. Tomczyk ◽  
David J. A. Evans ◽  
David H. Roberts ◽  
Wojciech Ewertowski
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicles ◽  
Structure From Motion ◽  
Low Cost ◽  
Control Point ◽  
Glacial Geomorphology ◽  
High Resolution Mapping ◽  
Aerial Vehicles ◽  
Resolution Mapping ◽  
Very High

This study presents the operational framework for rapid, very-high resolution mapping of glacial geomorphology, with the use of budget Unmanned Aerial Vehicles and a structure-from-motion approach. The proposed workflow comprises seven stages: (1) Preparation and selection of the appropriate platform; (2) transport; (3) preliminary on-site activities (including optional ground-control-point collection); (4) pre-flight setup and checks; (5) conducting the mission; (6) data processing; and (7) mapping and change detection. The application of the proposed framework has been illustrated by a mapping case study on the glacial foreland of Hørbyebreen, Svalbard, Norway. A consumer-grade quadcopter (DJI Phantom) was used to collect the data, while images were processed using the structure-from-motion approach. The resultant orthomosaic (1.9 cm ground sampling distance—GSD) and digital elevation model (7.9 cm GSD) were used to map the glacial-related landforms in detail. It demonstrated the applicability of the proposed framework to map and potentially monitor detailed changes in a rapidly evolving proglacial environment, using a low-cost approach. Its coverage of multiple aspects ensures that the proposed framework is universal and can be applied in a broader range of settings.

scholarly journals Method for Estimating Solar Energy Potential Based on Photogrammetry from Unmanned Aerial Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2144
Author(s):  
Jose Eduardo Fuentes ◽  
Francisco David Moya ◽  
Oscar Danilo Montoya
Keyword(s):  
Solar Energy ◽  
Unmanned Aerial Vehicles ◽  
3D Model ◽  
Low Cost ◽  
Atmospheric Conditions ◽  
Solar Panels ◽  
Energy Potential ◽  
Aerial Vehicles ◽  
3D Data ◽  
Surface Models

This study presents a method to estimate the solar energy potential based on 3D data taken from unmanned aerial devices. The solar energy potential on the roof of a building was estimated before the placement of solar panels using photogrammetric data analyzed in a geographic information system, and the predictions were compared with the data recorded after installation. The areas of the roofs were chosen using digital surface models and the hemispherical viewshed algorithm, considering how the solar radiation on the roof surface would be affected by the orientation of the surface with respect to the sun, the shade of trees, surrounding objects, topography, and the atmospheric conditions. The results show that the efficiency percentages of the panels and the data modeled by the proposed method from surface models are very similar to the theoretical efficiency of the panels. Radiation potential can be estimated from photogrammetric data and a 3D model in great detail and at low cost. This method allows the estimation of solar potential as well as the optimization of the location and orientation of solar panels.

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