Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Sensors and Algorithms for Autonomous Navigation of Aircraft
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sensors and Algorithms for Autonomous Navigation of Aircraft

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 7005

Special Issue Editors

Dr. Roberto Opromolla

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Naples Federico II, Piazzale Tecchio 80, 80125 Naples, Italy
Interests: spacecraft guidance; navigation and control; spacecraft relative navigation; pose determination; electro-optical sensors; LIDAR; star tracker; unmanned aerial vehicles; autonomous navigation; sense and avoid; visual detection and tracking
Special Issues, Collections and Topics in MDPI journals
Dr. Giancarmine Fasano

E-Mail Website
Guest Editor
Department of Industrial Engineering, University of Naples "Federico II", P.le Tecchio 80, 80125 Naples, Italy
Interests: unmanned aircraft systems; vision-based applications; avionics; guidance and navigation; detect and avoid; target tracking; path planning; data fusion; swarms; distributed space systems; formation flying; in orbit proximity operations; space surveillance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Collegues,

 

Navigation, intended as the collection, processing, and fusion of raw data from sensors and other information sources to estimate both the translational and rotational state of a vehicle, is a key functionality which needs to be improved to increase the autonomy level of future aircraft in general, and in particular, for Unmanned Aircraft Systems (UAS). In fact, highly accurate state estimates are required not only to enable safe UAS flight even in harsh environments, such as GNSS-challenging areas or disaster scenarios, but also to support mission objectives, like the generation of accurate maps.

Besides these navigation needs, future airborne navigation systems should also provide advanced autonomous functionalities in terms of environment perception and situational awareness, such as object classification, detection and tracking of static and moving obstacles, simultaneous localization, and mapping. Indeed, these capabilities shall enable aircraft to carry out advanced missions requiring critical decisions to be made in real time without humans in the loop.

In this framework, this Special Issue welcomes original research contributions and state-of-the-art reviews from academia and industry, regarding innovative technologies and algorithms aimed at improving autonomous navigation capabilities of future manned and unmanned aircraft. Contributions are welcomed either highlighting the role of new sensor systems, such as electro-optical, radar, LiDAR, inertial sensors, and magnetometers, or presenting new algorithms for raw data processing, state estimation, and robust environment perception also based on data fusion. The Special Issue topics include but are not limited to:

  • multi-sensor data fusion for state estimation
  • UAS navigation in challenging areas
  • simultaneous localization and mapping
  • autonomous take-off and landing
  • sensor-based airborne object detection, tracking, and classification
  • sensors and algorithms for sense and avoidance
  • sensors and algorithms for cooperative and opportunistic navigation
  • GNSS-resilient navigation and alternative positioning navigation and timing (A-PNT) architectures

Prof. Dr. Roberto Opromolla
Prof. Dr. Giancarmine Fasano
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.

Keywords

  • autonomous navigation
  • autonomous aircraft
  • Unmanned Aircraft Systems
  • SLAM
  • obstacle detection
  • sense and avoid
  • sensor data fusion
  • perception algorithms
  • A-PNT
  • detection
  • tracking
  • classification

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

29 pages, 6664 KiB  
Article
Parallel Cooperative Coevolutionary Grey Wolf Optimizer for Path Planning Problem of Unmanned Aerial Vehicles
by Raja Jarray, Mujahed Al-Dhaifallah, Hegazy Rezk and Soufiene Bouallègue
Sensors 2022, 22(5), 1826; https://doi.org/10.3390/s22051826 - 25 Feb 2022
Cited by 26 | Viewed by 2034
Abstract
The path planning of Unmanned Aerial Vehicles (UAVs) is a complex and hard task that can be formulated as a Large-Scale Global Optimization (LSGO) problem. A higher partition of the flight environment leads to an increase in route’s accuracy but at the expense [...] Read more.
The path planning of Unmanned Aerial Vehicles (UAVs) is a complex and hard task that can be formulated as a Large-Scale Global Optimization (LSGO) problem. A higher partition of the flight environment leads to an increase in route’s accuracy but at the expense of greater planning complexity. In this paper, a new Parallel Cooperative Coevolutionary Grey Wolf Optimizer (PCCGWO) is proposed to solve such a planning problem. The proposed PCCGWO metaheuristic applies cooperative coevolutionary concepts to ensure an efficient partition of the original search space into multiple sub-spaces with reduced dimensions. The decomposition of the decision variables vector into several sub-components is achieved and multi-swarms are created from the initial population. Each sub-swarm is then assigned to optimize a part of the LSGO problem. To form the complete solution, the representatives from each sub-swarm are combined. To reduce the computation time, an efficient parallel master-slave model is introduced in the proposed parameters-free PCCGWO. The master will be responsible for decomposing the original problem and constructing the context vector which contains the complete solution. Each slave is designed to evolve a sub-component and will send the best individual as its representative to the master after each evolutionary cycle. Demonstrative results show the effectiveness and superiority of the proposed PCCGWO-based planning technique in terms of several metrics of performance and nonparametric statistical analyses. These results show that the increase in the number of slaves leads to a more efficient result as well as a further improved computational time. Full article
(This article belongs to the Special Issue Sensors and Algorithms for Autonomous Navigation of Aircraft)
►▼ Show Figures

Figure 1

20 pages, 7136 KiB  
Article
VIAE-Net: An End-to-End Altitude Estimation through Monocular Vision and Inertial Feature Fusion Neural Networks for UAV Autonomous Landing
by Xupei Zhang, Zhanzhuang He, Zhong Ma, Peng Jun and Kun Yang
Sensors 2021, 21(18), 6302; https://doi.org/10.3390/s21186302 - 20 Sep 2021
Cited by 2 | Viewed by 2565
Abstract
Altitude estimation is one of the fundamental tasks of unmanned aerial vehicle (UAV) automatic navigation, where it aims to accurately and robustly estimate the relative altitude between the UAV and specific areas. However, most methods rely on auxiliary signal reception or expensive equipment, [...] Read more.
Altitude estimation is one of the fundamental tasks of unmanned aerial vehicle (UAV) automatic navigation, where it aims to accurately and robustly estimate the relative altitude between the UAV and specific areas. However, most methods rely on auxiliary signal reception or expensive equipment, which are not always available, or applicable owing to signal interference, cost or power-consuming limitations in real application scenarios. In addition, fixed-wing UAVs have more complex kinematic models than vertical take-off and landing UAVs. Therefore, an altitude estimation method which can be robustly applied in a GPS denied environment for fixed-wing UAVs must be considered. In this paper, we present a method for high-precision altitude estimation that combines the vision information from a monocular camera and poses information from the inertial measurement unit (IMU) through a novel end-to-end deep neural network architecture. Our method has numerous advantages over existing approaches. First, we utilize the visual-inertial information and physics-based reasoning to build an ideal altitude model that provides general applicability and data efficiency for neural network learning. A further advantage is that we have designed a novel feature fusion module to simplify the tedious manual calibration and synchronization of the camera and IMU, which are required for the standard visual or visual-inertial methods to obtain the data association for altitude estimation modeling. Finally, the proposed method was evaluated, and validated using real flight data obtained during a fixed-wing UAV landing phase. The results show the average estimation error of our method is less than 3% of the actual altitude, which vastly improves the altitude estimation accuracy compared to other visual and visual-inertial based methods. Full article
(This article belongs to the Special Issue Sensors and Algorithms for Autonomous Navigation of Aircraft)
►▼ Show Figures

Figure 1

22 pages, 4533 KiB  
Article
Onboard and External Magnetic Bias Estimation for UAS through CDGNSS/Visual Cooperative Navigation
by Federica Vitiello, Flavia Causa, Roberto Opromolla and Giancarmine Fasano
Sensors 2021, 21(11), 3582; https://doi.org/10.3390/s21113582 - 21 May 2021
Cited by 4 | Viewed by 1716
Abstract
This paper describes a calibration technique aimed at combined estimation of onboard and external magnetic disturbances for small Unmanned Aerial Systems (UAS). In particular, the objective is to estimate the onboard horizontal bias components and the external magnetic declination, thus improving heading estimation [...] Read more.
This paper describes a calibration technique aimed at combined estimation of onboard and external magnetic disturbances for small Unmanned Aerial Systems (UAS). In particular, the objective is to estimate the onboard horizontal bias components and the external magnetic declination, thus improving heading estimation accuracy. This result is important to support flight autonomy, even in environments characterized by significant magnetic disturbances. Moreover, in general, more accurate attitude estimates provide benefits for georeferencing and mapping applications. The approach exploits cooperation with one or more “deputy” UAVs and combines drone-to-drone carrier phase differential GNSS and visual measurements to attain magnetic-independent attitude information. Specifically, visual and GNSS information is acquired at different heading angles, and bias estimation is modelled as a non-linear least squares problem solved by means of the Levenberg–Marquardt method. An analytical error budget is derived to predict the achievable accuracy. The method is then demonstrated in flight using two customized quadrotors. A pointing analysis based on ground and airborne control points demonstrates that the calibrated heading estimate allows obtaining an angular error below 1°, thus resulting in a substantial improvement against the use of either the non-calibrated magnetic heading or the multi-sensor-based solution of the DJI onboard navigation filter, which determine angular errors of the order of several degrees. Full article
(This article belongs to the Special Issue Sensors and Algorithms for Autonomous Navigation of Aircraft)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop