scholarly journals Generic Component-Based Mission-Centric Energy Model for Micro-Scale Unmanned Aerial Vehicles

Drones ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 63
Author(s):  
Christoph Steup ◽  
Simon Parlow ◽  
Sebastian Mai ◽  
Sanaz Mostaghim
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Autonomous Systems ◽  
Energy Model ◽  
Generic Model ◽  
Extended Model ◽  
Generic Models ◽  
Aerial Vehicles ◽  
Proposed Model ◽  
Typical Behaviour ◽  
Set Up

The trend towards the usage of battery-electric unmanned aerial vehicles needs new strategies in mission planning and in the design of the systems themselves. To create an optimal mission plan and take appropriate decisions during the mission, a reliable, accurate and adaptive energy model is of utmost importance. However, most existing approaches either use very generic models or ones that are especially tailored towards a specific UAV. We present a generic energy model that is based on decomposing a robotic system into multiple observable components. The generic model is applied to a swarm of quadcopters and evaluated in multiple flights with different manoeuvres. We additionally use the data from practical experiments to learn and generate a mission-agnostic energy model which can match the typical behaviour of our quadcopters such as hovering; movement in x, y and z directions; landing; communication; and illumination. The learned energy model concurs with the overall energy consumption with an accuracy over 95% compared to the training flights for the indoor use case. An extended model reduces the error to less than 1.4%. Consequently, the proposed model enables an estimation of the energy used in flight and on the ground, which can be easily incorporated in autonomous systems and enhance decision-making with reliable input. The used learning mechanism allows to deploy the approach with minimal effort to new platforms needing only some representative test missions, which was shown using additional outdoor validation flights with a different quadcopter of the same build and the originally trained models. This set-up increased the prediction error of our model to 4.46%.

Detection and localization of helipad in autonomous UAV landing: a coupled visual-inertial approach with artificial intelligence

2020 ◽  
Vol 71 (7) ◽  
pp. 828-839
Author(s):  
Thinh Hoang Dinh ◽  
Hieu Le Thi Hong
Keyword(s):  
Neural Network ◽  
Unmanned Aerial Vehicles ◽  
Fiducial Marker ◽  
Localization Algorithm ◽  
Challenging Problem ◽  
Autonomous Landing ◽  
Aerial Vehicles ◽  
Set Up ◽  
Detection And Localization ◽  
Rotary Wing

Autonomous landing of rotary wing type unmanned aerial vehicles is a challenging problem and key to autonomous aerial fleet operation. We propose a method for localizing the UAV around the helipad, that is to estimate the relative position of the helipad with respect to the UAV. This data is highly desirable to design controllers that have robust and consistent control characteristics and can find applications in search – rescue operations. AI-based neural network is set up for helipad detection, followed by optimization by the localization algorithm. The performance of this approach is compared against fiducial marker approach, demonstrating good consensus between two estimations

scholarly journals The method of maximum plausibility in the problems of protection of the radio signal of unmanned aerial vehicles control

2020 ◽  
Vol 44 (4) ◽  
Author(s):  
S. I. Ganusyak ◽  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Radio Signal ◽  
Harmonic Oscillation ◽  
Radio Communication ◽  
Noise Interference ◽  
Communication Signal ◽  
Aerial Vehicles ◽  
Proposed Model ◽  
Method Of Maximum Likelihood ◽  
Aerial Vehicle

The paper considers the problem of protection of control of the radio signal of control of unmanned aerial vehicles as a task of control of signal parameters. The problem of control of a parameter of a radio signal and a problem of protection against interception by control of the unmanned aerial vehicle is formulated. The methods of estimating the parameters of the radio communication signal are analyzed and the method of estimating the parameters based on the method of maximum likelihood is proposed. This technique is demonstrated by estimating the phase of harmonic oscillation, which describes the radio signal and there are noise interference. Simulation modeling of the developed method is carried out, which confirmed the adequacy of the proposed model.

scholarly journals A Semi-Physical Platform for Guidance and Formations of Fixed-Wing Unmanned Aerial Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1136 ◽  
Author(s):  
Jun Yang ◽  
Arun Geo Thomas ◽  
Satish Singh ◽  
Simone Baldi ◽  
Ximan Wang
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Formation Control ◽  
Physical Simulation ◽  
3D Visualization ◽  
Autonomous Systems ◽  
Level Control ◽  
Control Laws ◽  
Ongoing Research ◽  
Control Loops ◽  
Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) have multi-domain applications, fixed-wing UAVs being a widely used class. Despite the ongoing research on the topics of guidance and formation control of fixed-wing UAVs, little progress is known on implementation of semi-physical validation platforms (software-in-the-loop or hardware-in-the-loop) for such complex autonomous systems. A semi-physical simulation platform should capture not only the physical aspects of UAV dynamics, but also the cybernetics aspects such as the autopilot and the communication layers connecting the different components. Such a cyber-physical integration would allow validation of guidance and formation control algorithms in the presence of uncertainties, unmodelled dynamics, low-level control loops, communication protocols and unreliable communication: These aspects are often neglected in the design of guidance and formation control laws for fixed-wing UAVs. This paper describes the development of a semi-physical platform for multi-fixed wing UAVs where all the aforementioned points are carefully integrated. The environment adopts Raspberry Pi’s programmed in C++, which can be interfaced to standard autopilots (PX4) as a companion computer. Simulations are done in a distributed setting with a server program designed for the purpose of routing data between nodes, handling the user inputs and configurations of the UAVs. Gazebo-ROS is used as a 3D visualization tool.

scholarly journals Pole Placement Technique Applied in Unmanned Aerial Vehicles Automatic Flight Control Systems Design

2018 ◽  
Vol 23 (1) ◽  
pp. 88-98 ◽  
Author(s):  
Róbert Szabolcsi
Keyword(s):  
Control System ◽  
Unmanned Aerial Vehicles ◽  
Flight Control ◽  
Systems Design ◽  
Pole Placement ◽  
Flight Control System ◽  
Aerial Vehicles ◽  
Control Systems Design ◽  
Set Up ◽  
Placement Technique

Abstract Unmanned aerial vehicles are widely spread and intensively used ones both in governmental and in private applications. The standard arrangements of the commercial-off-the-shelves unmanned aerial vehicles sometimes neglect application of the automatic flight control system onboard. However, there are many initiatives to ensure autonomous flights of the unmanned aerial vehicles via pre-programmed flight paths. Moreover, automatic flight control system can ensure necessary level of the flight safety both in VFR and IFR flights. The aim of this study is to guide UAV users in set up commercial onboard autopilots available on the market. On the contrary, fitness of the autopilot to a given type of the air robot is not guaranteed, and, an extra load on users can appear in controller settings. The proposed pole placement technique is one of the proper methods eliminating difficulties, and, computer aided gain selection using MATLAB will be presented.

scholarly journals Evaluation of MAV/UAV Collaborative Combat Capability Based on Network Structure

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Jieru Fan ◽  
Dongguang Li ◽  
Rupeng Li
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Evaluation Method ◽  
Quantitative Description ◽  
Collaborative Network ◽  
Application Research ◽  
Military Research ◽  
Aerial Vehicles ◽  
Proposed Model ◽  
Process Node ◽  
Control Capability

The collaborative combat of manned/unmanned aerial vehicles (MAVs/UAVs) is a popular topic in combat application research. It maximizes the autonomous combat capability of UAVs and the control capability of MAVs. Furthermore, it improves the comprehensive combat effectiveness. The quantitative description of intercommunication in different aircrafts along with the evaluation of the collaborative combat capability is an emphasis in military research. This paper analyzes the collaborative combat process. Node and edge models are established in the MAV/UAV collaborative network. The intercommunication and combat behaviors among combat entities are analyzed. Based on the information entropy, the effect of capability uncertainties on the collaborative combat is described quantitatively. An evaluation method of the MAV/UAV collaborative combat capability is proposed. Finally, an example is given to demonstrate the proposed model and evaluation method that prove its feasibility and effectiveness.

scholarly journals METHODS AND MEANS OF COUNTERACTION OF ACOUSTIC AND OPTICAL RECONNAISSANCE UAV

2020 ◽  
Vol 6 (1) ◽  
pp. 149-154
Author(s):  
Nikita V. Ignatenko ◽  
Alexey N. Polikanin
Keyword(s):  
Deep Learning ◽  
Unmanned Aerial Vehicles ◽  
Autonomous Systems ◽  
Multiple Objects ◽  
Multiple Sensors ◽  
Aerial Vehicles ◽  
Main Computer ◽  
Computer Attacks ◽  
Control Technologies ◽  
Combining Information

For the last few years, the ease of purchasing and using unmanned aerial vehicles (UAVs), their affordable cost has increased the demand for them both by companies and individuals. However, these devices might carry out illegal actions, starting with smuggling of illegal goods, unauthorized intelligence and computer attacks. As a result, this led to the urgency of developing effective and available countermeasures to detect and neutralize drones that perform reconnaissance of objects with confidential information. The most successful are autonomous systems for detecting and suppressing drones, which include optoelectronic, acoustic radar and radio frequency sensors, information from which is combined on the main computer to identify the threat and make further decisions. However, real-time monitoring is a rather difficult process that requires timely detection of adverse events or conditions. This creates many complex tasks such as object detection, classification, tracking multiple objects, and combining information from multiple sensors. In recent years, researchers have used various techniques to solve these problems and made notable progress. Applying deep learning to detect and classify UAVs is considered a new concept. In this regard, it became necessary to provide a generalized overview of UAV control technologies used for reconnaissance.

scholarly journals The model of UAV formation based on the uniform allocation of points on the sphere

2018 ◽  
Vol 161 ◽  
pp. 03022 ◽  
Author(s):  
Mikhail Khachumov ◽  
Vyacheslav Khachumov
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Uniform Distribution ◽  
Mathematical Apparatus ◽  
Optimal Assignment ◽  
Aerial Vehicles ◽  
Proposed Model ◽  
Distribution Of Points ◽  
Clustering Problems

We consider an approach to constructing the desired virtual structure, which should be formed by unmanned aerial vehicles (UAVs). The proposed model is based on the principle of quasi-uniform allocation of points, previously used by the author in clustering problems. The mathematical apparatus for solving the problem of forming the desired structure is given: necessary theorems are proved; the hypothesis on the uniform distribution of points on the boundary of the circle and the sphere is put forward and partially proved. The method for optimal assignment of UAVs to goal positions in the desired formation is considered.

Generic Model for Energy Carbon Emission of Building Materials

2010 ◽  
Vol 168-170 ◽  
pp. 365-368
Author(s):  
Meng Liu ◽  
Yong Qiang Li ◽  
Xiang Zhan
Keyword(s):  
Carbon Emission ◽  
Large Scale ◽  
Building Materials ◽  
Model Building ◽  
Generic Model ◽  
Analytic Model ◽  
Generic Models ◽  
Life Cycle Theory ◽  
Rolling Strip ◽  
Proposed Model

To establish an analytic model for carbon emission of building lifecycle, based on life cycle theory, a generic model for energy carbon emission of building materials was proposed. With the model, building materials were classified into several categories, such as cement, steel etc. Furthermore, some product from each category, such as PS325 in cement, was selected as a benchmark of this category. And then product coefficient and region coefficient were induced to express the differences in products and regions or manufacturers. With the proposed model, generic models for cement and steel were established. And it is found that energy carbon emission intensity of steel is around 30~40 times than that of cement. Energy carbon emission of PI525 and PO425 are 51% and 34% more than that of PS325, respectively. Carbon emission of large-scale steel and cold rolling strip is the most emitter in steel products.

scholarly journals A Psychoacoustic Approach to Building Knowledge about Human Response to Noise of Unmanned Aerial Vehicles

2021 ◽  
Vol 18 (2) ◽  
pp. 682
Author(s):  
Antonio J. Torija ◽  
Charlotte Clark
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Noise Exposure ◽  
Autonomous Systems ◽  
Public Health Issue ◽  
Human Response ◽  
Noise Sources ◽  
Governmental Agencies ◽  
Aerial Vehicles ◽  
Operational Characteristics ◽  
Significant Public Health

We are on the cusp of a revolution in the aviation sector, driven by the significant progress in electric power and battery technologies, and autonomous systems. Several industry leaders and governmental agencies are currently investigating the use of Unmanned Aerial Vehicles (UAVs), or “drones” as commonly known, for an ever-growing number of applications—from blue light services to parcel delivery and urban mobility. Undoubtedly, the operation of UAVs will lead to noise exposure, which has the potential to become a significant public health issue. This paper first describes the main acoustic and operational characteristics of UAVs, as an unconventional noise source compared to conventional civil aircraft. Gaps in the literature and the regulations on the noise metrics and acceptable noise levels are identified and discussed. The state-of-the-art evidence on human response to aircraft and other environmental noise sources is reviewed and its application for UAVs discussed. A methodological framework is proposed for building psychoacoustic knowledge, to inform systems and operations development to limit the noise impact on communities.

scholarly journals ViTT: Vision Transformer Tracker

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5608
Author(s):  
Xiaoning Zhu ◽  
Yannan Jia ◽  
Sun Jian ◽  
Lize Gu ◽  
Zhang Pu
Keyword(s):  
Computer Vision ◽  
Unmanned Aerial Vehicles ◽  
Language Processing ◽  
Global Context ◽  
Aerial Vehicles ◽  
Object Locations ◽  
Proposed Model ◽  
Shared Network ◽  
Complex Computer ◽  
Multi Uav

This paper presents a new model for multi-object tracking (MOT) with a transformer. MOT is a spatiotemporal correlation task among interest objects and one of the crucial technologies of multi-unmanned aerial vehicles (Multi-UAV). The transformer is a self-attentional codec architecture that has been successfully used in natural language processing and is emerging in computer vision. This study proposes the Vision Transformer Tracker (ViTT), which uses a transformer encoder as the backbone and takes images directly as input. Compared with convolution networks, it can model global context at every encoder layer from the beginning, which addresses the challenges of occlusion and complex scenarios. The model simultaneously outputs object locations and corresponding appearance embeddings in a shared network through multi-task learning. Our work demonstrates the superiority and effectiveness of transformer-based networks in complex computer vision tasks and paves the way for applying the pure transformer in MOT. We evaluated the proposed model on the MOT16 dataset, achieving 65.7% MOTA, and obtained a competitive result compared with other typical multi-object trackers.

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