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Application of Autonomous Unmanned Aircraft Systems (UAS) in Intelligent Sensing
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Application of Autonomous Unmanned Aircraft Systems (UAS) in Intelligent Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 5604

Special Issue Editors

Dr. Boyang Li

E-Mail Website
Guest Editor
Department of Aeronautical and Aviation Engineering,The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Interests: unmanned aerial vehicle; flight dynamics and control; aerial robotics; SLAM
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Carlos Tavares Calafate

E-Mail Website
Guest Editor
Computer Engineering Department (DISCA), Universitat Politècnica de València (UPV), 46022 Valencia, Spain
Interests: UAV networking and communications solutions; drone swarms; UAS traffic management (UTM); IoT; Traffic management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Applying autonomous unmanned aircraft systems (UAS) for sensing applications could significantly improve the efficiency, reduce the costs, and lower the risks that are commonly involved in these types of applications. With the fast developments that have been seen in UAS/UAV platform design, flight control systems, localization, and navigation algorithms as well as in sensor technology, autonomous UAS has become a promising sensing platform that can be used in various applications, such as environmental monitoring and infrastructure inspection. These systems can reduce the necessity of traditional manual inspection in risky working environments and can avoid the costs that are associated with the use of piloted fixed-wing aircrafts or helicopters to conduct large-scale sensing tasks.

New UAV platforms, localization and planning methods, aerial-based sensors, and learning-based data processing capabilities provide both opportunities and challenges that invite the research community to provide novel solutions. The key aim of this Special Issue is to bring together innovative research that uses off-the-shelf or custom-made platforms to extend autonomous aerial sensing capabilities. Contributions from all fields that are related to the UAS/UAV in sensing applications are of interest, including, but not limited to, the following topics:

  • Unmanned aircraft systems (UAS)/unmanned aerial vehicle (UAV) platform design;
  • Intelligent sensing technologies;
  • Aerial-based environment monitoring;
  • Aerial-based infrastructure inspection;
  • Autonomous system development;
  • Localization, mapping, and planning;
  • Multi-agent collaboration;
  • Learning-based data processing.

Dr. Boyang Li
Prof. Dr. Carlos Tavares Calafate
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

24 pages, 4914 KiB  
Article
Adaptive UAV Navigation Method Based on AHRS
by Yin Lu, Zhipeng Li, Jun Xiong and Ke Lv
Sensors 2024, 24(8), 2518; https://doi.org/10.3390/s24082518 - 14 Apr 2024
Viewed by 388
Abstract
To address the inaccuracy of the Constant Acceleration/Constant Velocity (CA/CV) model as the state equation in describing the relative motion state in UAV relative navigation, an adaptive UAV relative navigation method is proposed, which is based on the UAV attitude information provided by [...] Read more.
To address the inaccuracy of the Constant Acceleration/Constant Velocity (CA/CV) model as the state equation in describing the relative motion state in UAV relative navigation, an adaptive UAV relative navigation method is proposed, which is based on the UAV attitude information provided by Attitude and Heading Reference System (AHRS). The proposed method utilizes the AHRS output attitude parameters as the benchmark for dead reckoning and derives a relative navigation state equation with attitude error as process noise. By integrating the extended Kalman filter output for relative state estimation and employing an adaptive decision rule designed using the innovation of the filter update phase, the proposed method recalculates motion states deviating from the actual motion using the Tasmanian Devil Optimization (TDO) algorithm. The simulation results show that, compared with the CA/CV model, the proposed method reduces the relative position errors by 12%, 23%, and 32% in the X, Y, and Z directions, respectively, and that it reduces the relative velocity errors by 350%, 330%, and 300%, respectively. There is a significant improvement in the relative navigation accuracy. Full article
(This article belongs to the Special Issue Application of Autonomous Unmanned Aircraft Systems (UAS) in Intelligent Sensing)
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18 pages, 636 KiB  
Article
Stackelberg Game Approach for Service Selection in UAV Networks
by Abdessalam Mohammed Hadjkouider, Chaker Abdelaziz Kerrache, Ahmed Korichi, Yesin Sahraoui and Carlos T. Calafate
Sensors 2023, 23(9), 4220; https://doi.org/10.3390/s23094220 - 23 Apr 2023
Cited by 1 | Viewed by 1270
Abstract
Nowadays, mobile devices are expected to perform a growing number of tasks, whose complexity is also increasing significantly. However, despite great technological improvements in the last decade, such devices still have limitations in terms of processing power and battery lifetime. In this context, [...] Read more.
Nowadays, mobile devices are expected to perform a growing number of tasks, whose complexity is also increasing significantly. However, despite great technological improvements in the last decade, such devices still have limitations in terms of processing power and battery lifetime. In this context, mobile edge computing (MEC) emerges as a possible solution to address such limitations, being able to provide on-demand services to the customer, and bringing closer several services published in the cloud with a reduced cost and fewer security concerns. On the other hand, Unmanned Aerial Vehicle (UAV) networking emerged as a paradigm offering flexible services, new ephemeral applications such as safety and disaster management, mobile crowd-sensing, and fast delivery, to name a few. However, to efficiently use these services, discovery and selection strategies must be taken into account. In this context, discovering the services made available by a UAV-MEC network, and selecting the best services among those available in a timely and efficient manner, can become a challenging task. To face these issues, game theory methods have been proposed in the literature that perfectly suit the case of UAV-MEC services by modeling this challenge as a Stackelberg game, and using existing approaches to find the solution for such a game aiming at an efficient services’ discovery and service selection. Hence, the goal of this paper is to propose Stackelberg-game-based solutions for service discovery and selection in the context of UAV-based mobile edge computing. Simulations results conducted using the NS-3 simulator highlight the efficiency of our proposed game in terms of price and QoS metrics. Full article
(This article belongs to the Special Issue Application of Autonomous Unmanned Aircraft Systems (UAS) in Intelligent Sensing)
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21 pages, 22911 KiB  
Article
UAV-Based Smart Educational Mechatronics System Using a MoCap Laboratory and Hardware-in-the-Loop
by Luis F. Luque-Vega, Emmanuel Lopez-Neri, Carlos A. Arellano-Muro, Luis E. González-Jiménez, Jawhar Ghommam, Maarouf Saad, Rocío Carrasco-Navarro, Riemann Ruíz-Cruz and Héctor A. Guerrero-Osuna
Sensors 2022, 22(15), 5707; https://doi.org/10.3390/s22155707 - 30 Jul 2022
Cited by 6 | Viewed by 2767
Abstract
Within Industry 4.0, drones appear as intelligent devices that have brought a new range of innovative applications to the industrial sector. The required knowledge and skills to manage and appropriate these technological devices are not being developed in most universities. This paper presents [...] Read more.
Within Industry 4.0, drones appear as intelligent devices that have brought a new range of innovative applications to the industrial sector. The required knowledge and skills to manage and appropriate these technological devices are not being developed in most universities. This paper presents an unmanned aerial vehicle (UAV)-based smart educational mechatronics system that makes use of a motion capture (MoCap) laboratory and hardware-in-the-loop (HIL) to teach UAV knowledge and skills, within the Educational Mechatronics Conceptual Framework (EMCF). The macro-process learning construction of the EMCF includes concrete, graphic, and abstract levels. The system comprises a DJI Phantom 4, a MoCap laboratory giving the drone location, a Simulink drone model, and an embedded system for performing the HIL simulation. The smart educational mechatronics system strengthens the assimilation of the UAV waypoint navigation concept and the capacity for drone flight since it permits the validation of the physical drone model and testing of the trajectory tracking control. Moreover, it opens up a new range of possibilities in terms of knowledge construction through best practices, activities, and tasks, enriching the university courses. Full article
(This article belongs to the Special Issue Application of Autonomous Unmanned Aircraft Systems (UAS) in Intelligent Sensing)
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