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Drones
Special Issues
Drones Navigation and Orientation
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Drones Navigation and Orientation

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 27721

Special Issue Editors

Prof. Dr. Costas Armenakis

E-Mail Website
Guest Editor
Geomatics Engineering, Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, Ontario, ON M3J 1P3, Canada
Interests: photogrammetric engineering; remote sensing mapping; low-cost unmanned mobile mapping systems; indoor/outdoor navigation and mapping; sensor integration; 3D modelling using optical and lidar data; high resolution imagery; spatial data co-registration; spatial awareness and intelligence; GIS; risk assessment; disaster management
Special Issues, Collections and Topics in MDPI journals
Dr. Ismael Colomina

E-Mail Website
Guest Editor
GeoNumerics, 08860 Castelldefels, Barcelona, Spain
Interests: geomatics; navigation; geometric photogrammetry and remote sensing; sensor modelling; sensor integration; sensor orientation and calibration; multi-sensor integration for orientation and navigation; indoor/outdoor navigation; estimation methods; software systems for orientation and navigation; unmanned aerial systems; unmanned aerial systems for photogrammetry, remote sensing and mapping; combined terrestrial and aerial mobile mapping systems

Special Issue Information

Dear Colleagues,

The development of semi-autonomous and autonomous unmanned aerial systems, also known as drones, and their applications is experiencing continuous growth. Drones’ success has resulted in increased interest in other types of uncrewed vehicles, from submarines to planetary ground vehicles. Currently, the category ‘drone’ embraces all types of uncrewed vehicles, from underwater autonomous ones to planetary robots (excluding driverless cars).

Autonomous operation of drones in known and unknown environments requires continuous integration of navigation sensors data, perception of environmental elements with respect to space and time, understanding of the scene situation through data and information, anticipation of the next stages, and the ability to make quick knowledge-based decisions based on real-time position and navigation, scene sensing, recognition, understanding and visualization.

For most drone applications, the vehicle’s position and attitude—the drone’s orientation—is required to exploit the collected data. For all drone operations, knowing the drone orientation in real time—drone navigation—is required. Navigation is a fundamental function of drone guidance, control, and navigation (GNC) systems, often referred to as autopilots. In turn, GNC systems are a necessary subsystem of drones. Both drone orientation and navigation must meet certain performance specifications, often revolving around precision, accuracy, integrity, availability and continuity. The latter four are called the required navigation performance (RNP) parameters, and they define a family of navigation specifications.

The RNP family of specifications continues to grow alongside the advent of new drone applications. Where and when GNSS signals are available, RNPs often challenge GNSS performance. Otherwise, other (direct or indirect) motion-sensing technologies and methods must be used. Ultimately, multi-sensor systems and sensor fusion methods for orientation and navigation are at the very core of all types of drones, and this is an active field of research.

This Special Issue of Drones targets both real-time and post-mission determination of drone trajectories; including but not limited to uncrewed aerial systems and adds to the already interesting collection of related articles and Special Issues of the journal including the previous Special Issue devoted to UAS Navigation and Orientation.

We encourage the submission of quality, innovative articles covering overarching topics such as real-time robust navigation, high-accuracy post-mission orientation, small and large drones, and applications varying from autonomous land, underwater and aerial to planetary robotic platforms. Applications such drone-based photogrammetry and mapping, as well as developments pertaining to orientation and navigation performance when using the new GNSS signals and augmentation services, are also welcome.

We encourage submissions which present the most recent advancements in all aspects of Drone Navigation and Orientation, including (but not limited to):

  • Methods of extracting drone navigation and orientation user RNPs.
  • Direct, indirect and integrated sensor/platform positioning and orientation.
  • Vision-based navigation and orientation.
  • Single/multiple IMU–vision-based navigation and orientation.
  • Simultaneous Localization and Mapping (SLAM) methods.
  • Navigation and orientation using machine learning and deep learning.
  • Advanced techniques for motion sensor data fusion.
  • Computational aspects and incremental approaches.
  • Collaborative navigation of homogeneous and heterogeneous drone swarms.
  • Navigation and orientation in outdoors and indoors environments.
  • Autonomous navigation.
  • Obstacle detection and avoidance.
  • Precise Point Positioning-based (PPP) navigation.
  • Ultra-wideband (UWB) localization.
  • Optical flow-based vision-based navigation.
  • Trajectory tracking systems.
  • Autopilots and navigation: standard and advanced solutions for navigation integrity.
  • Path planning.

Beyond visual line of sight (BVLOS) navigation.

Prof. Dr. Costas Armenakis
Dr. Ismael Colomina
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. Drones is an international peer-reviewed open access monthly 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 (6 papers)

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Research

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18 pages, 5430 KiB  
Article
Three-Dimensional Indoor Positioning Scheme for Drone with Fingerprint-Based Deep-Learning Classifier
by Shuzhi Liu, Houjin Lu and Seung-Hoon Hwang
Drones 2024, 8(1), 15; https://doi.org/10.3390/drones8010015 - 09 Jan 2024
Viewed by 1435
Abstract
Unmanned aerial vehicles (UAVs) hold significant potential for various indoor applications, such as mapping, surveillance, navigation, and search and rescue operations. However, indoor positioning is a significant challenge for UAVs, owing to the lack of GPS signals and the complexity of indoor environments. [...] Read more.
Unmanned aerial vehicles (UAVs) hold significant potential for various indoor applications, such as mapping, surveillance, navigation, and search and rescue operations. However, indoor positioning is a significant challenge for UAVs, owing to the lack of GPS signals and the complexity of indoor environments. Therefore, this study was aimed at developing a Wi-Fi-based three-dimensional (3D) indoor positioning scheme tailored to time-varying environments, involving human movement and uncertainties in the states of wireless devices. Specifically, we established an innovative 3D indoor positioning system to meet the localisation demands of UAVs in indoor environments. A 3D indoor positioning database was developed using a deep-learning classifier, enabling 3D indoor positioning through Wi-Fi technology. Additionally, through a pioneering integration of fingerprint recognition into wireless positioning technology, we enhanced the precision and reliability of indoor positioning through a detailed analysis and learning process of Wi-Fi signal features. Two test cases (Cases 1 and 2) were designed with positioning height intervals of 0.5 m and 0.8 m, respectively, corresponding to the height of the test scene for positioning simulation and testing. With an error margin of 4 m, the simulation accuracies for the (X, Y) dimension reached 94.08% (Case 1) and 94.95% (Case 2). When the error margin was 0 m, the highest simulation accuracies for the H dimension were 91.84% (Case 1) and 93.61% (Case 2). Moreover, 40 real-time positioning experiments were conducted in the (X, Y, H) dimension. In Case 1, the average positioning success rates were 50.8% (Margin-0), 72.9% (Margin-1), and 81.4% (Margin-2), and the corresponding values for Case 2 were 52.4%, 74.5%, and 82.8%, respectively. The results demonstrated that the proposed method can facilitate 3D indoor positioning based only on Wi-Fi technologies. Full article
(This article belongs to the Special Issue Drones Navigation and Orientation)
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22 pages, 10296 KiB  
Article
Unmanned Aerial Vehicle 3D Path Planning Based on an Improved Artificial Fish Swarm Algorithm
by Tao Zhang, Liya Yu, Shaobo Li, Fengbin Wu, Qisong Song and Xingxing Zhang
Drones 2023, 7(10), 636; https://doi.org/10.3390/drones7100636 - 16 Oct 2023
Cited by 1 | Viewed by 1682
Abstract
A well-organized path can assist unmanned aerial vehicles (UAVs) in performing tasks efficiently. The artificial fish swarm algorithm (AFSA) is a widely used intelligent optimization algorithm. However, the traditional AFSA exhibits issues of non-uniform population distribution and susceptibility to local optimization. Despite the [...] Read more.
A well-organized path can assist unmanned aerial vehicles (UAVs) in performing tasks efficiently. The artificial fish swarm algorithm (AFSA) is a widely used intelligent optimization algorithm. However, the traditional AFSA exhibits issues of non-uniform population distribution and susceptibility to local optimization. Despite the numerous AFSA variants introduced in recent years, many of them still grapple with challenges like slow convergence rates. To tackle the UAV path planning problem more effectively, we present an improved AFSA algorithm (IAFSA), which is primarily rooted in the following considerations: (1) The prevailing AFSA variants have not entirely resolved concerns related to population distribution disparities and a predisposition for local optimization. (2) Recognizing the specific demands of the UAV path planning problem, an algorithm that can combine global search capabilities with swift convergence becomes imperative. To evaluate the performance of IAFSA, it was tested on 10 constrained benchmark functions from CEC2020; the effectiveness of the proposed strategy is verified on the UAV 3D path planning problem; and comparative algorithmic experiments of IAFSA are conducted in different maps. The results of the comparison experiments show that IAFSA has high global convergence ability and speed. Full article
(This article belongs to the Special Issue Drones Navigation and Orientation)
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14 pages, 2612 KiB  
Article
Photometric Long-Range Positioning of LED Targets for Cooperative Navigation in UAVs
by Laurent Jospin, Alexis Stoven-Dubois and Davide Antonio Cucci
Drones 2019, 3(3), 69; https://doi.org/10.3390/drones3030069 - 30 Aug 2019
Cited by 5 | Viewed by 3761
Abstract
Autonomous flight with unmanned aerial vehicles (UAVs) nowadays depends on the availability and reliability of Global Navigation Satellites Systems (GNSS). In cluttered outdoor scenarios, such as narrow gorges, or near tall artificial structures, such as bridges or dams, reduced sky visibility and multipath [...] Read more.
Autonomous flight with unmanned aerial vehicles (UAVs) nowadays depends on the availability and reliability of Global Navigation Satellites Systems (GNSS). In cluttered outdoor scenarios, such as narrow gorges, or near tall artificial structures, such as bridges or dams, reduced sky visibility and multipath effects compromise the quality and the trustworthiness of the GNSS position fixes, making autonomous, or even manual, flight difficult and dangerous. To overcome this problem, cooperative navigation has been proposed: a second UAV flies away from any occluding objects and in line of sight from the first and provides the latter with positioning information, removing the need for full and reliable GNSS coverage in the area of interest. In this work we use high-power light-emitting diodes (LEDs) to signalize the second drone and we present a computer vision pipeline that allows to track the second drone in real-time from a distance up to 100 m and to compute its relative position with decimeter accuracy. This is based on an extension to the classical iterative algorithm for the Perspective-n-Points problem in which the photometric error is minimized according to a image formation model. This extension allow to substantially increase the accuracy of point-feature measurements in image space (up to 0.05 pixels), which directly translates into higher positioning accuracy with respect to conventional methods. Full article
(This article belongs to the Special Issue Drones Navigation and Orientation)
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18 pages, 4441 KiB  
Article
Towards a Model Based Sensor Measurement Variance Input for Extended Kalman Filter State Estimation
by Harry A. G. Pointon, Benjamin J. McLoughlin, Christian Matthews and Frederic A. Bezombes
Drones 2019, 3(1), 19; https://doi.org/10.3390/drones3010019 - 14 Feb 2019
Cited by 14 | Viewed by 4362
Abstract
In this paper, we present an alternate method for the generation and implementation of the sensor measurement variance used in an Extended Kalman Filter (EKF). Furthermore, it demonstrates the limitations of a conventional EKF implementation and postulates an alternate form for representing the [...] Read more.
In this paper, we present an alternate method for the generation and implementation of the sensor measurement variance used in an Extended Kalman Filter (EKF). Furthermore, it demonstrates the limitations of a conventional EKF implementation and postulates an alternate form for representing the sensor measurement variance by extending and improving the characterisation methodology presented in the previous work. As presented in earlier work, the use of surveying grade optical measurement instruments allows for a more effective characterisation of Ultra-Wide Band (UWB) localisation sensors; however, in cluttered environments, the sensor measurement variance will change, making this method not robust. To compensate for the noisier readings, an EKF using a model based sensor measurement variance was developed. This approach allows for a more accurate representation of the sensor measurement variance and leads to a more robust state estimation system. Simulations were run using synthetic data in order to test the effectiveness of the EKF against the originally developed EKF; next, the new EKF was compared to the original EKF using real world data. The new EKF was shown to function much more stably and consistently in less ideal environments for UWB deployment than the previous version. Full article
(This article belongs to the Special Issue Drones Navigation and Orientation)
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12 pages, 1977 KiB  
Article
Sensitivity to Time Delays in VDM-Based Navigation
by Gabriel Laupré, Mehran Khaghani and Jan Skaloud
Drones 2019, 3(1), 11; https://doi.org/10.3390/drones3010011 - 14 Jan 2019
Cited by 5 | Viewed by 4465
Abstract
A recently proposed navigation methodology for aerial platforms based on the vehicle dynamic model (VDM) has shown promising results in terms of navigation autonomy. Its practical realization requires that control inputs are related to the same absolute time frame as inertial measurement unit [...] Read more.
A recently proposed navigation methodology for aerial platforms based on the vehicle dynamic model (VDM) has shown promising results in terms of navigation autonomy. Its practical realization requires that control inputs are related to the same absolute time frame as inertial measurement unit (IMU) data and all other observations when available (e.g., global navigation satellite system (GNSS) position, barometric altitude, etc.). This study analyzes the (non-) tolerances of possible delays in control-input command with respect to navigation performance on a fixed-wing unmanned aerial vehicle (UAV). Multiple simulations using two emulated trajectories based on real flights reveal the vital importance of correct time-tagging of servo data while that of motor data turned out to be tolerable to a considerably large extent. Full article
(This article belongs to the Special Issue Drones Navigation and Orientation)
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Review

Jump to: Research

30 pages, 343 KiB  
Review
A Survey of Recent Extended Variants of the Traveling Salesman and Vehicle Routing Problems for Unmanned Aerial Vehicles
by Ines Khoufi, Anis Laouiti and Cedric Adjih
Drones 2019, 3(3), 66; https://doi.org/10.3390/drones3030066 - 24 Aug 2019
Cited by 96 | Viewed by 9859
Abstract
The use of Unmanned Aerial Vehicles (UAVs) is rapidly growing in popularity. Initially introduced for military purposes, over the past few years, UAVs and related technologies have successfully transitioned to a whole new range of civilian applications such as delivery, logistics, surveillance, entertainment, [...] Read more.
The use of Unmanned Aerial Vehicles (UAVs) is rapidly growing in popularity. Initially introduced for military purposes, over the past few years, UAVs and related technologies have successfully transitioned to a whole new range of civilian applications such as delivery, logistics, surveillance, entertainment, and so forth. They have opened new possibilities such as allowing operation in otherwise difficult or hazardous areas, for instance. For all applications, one foremost concern is the selection of the paths and trajectories of UAVs, and at the same time, UAVs control comes with many challenges, as they have limited energy, limited load capacity and are vulnerable to difficult weather conditions. Generally, efficiently operating a drone can be mathematically formalized as a path optimization problem under some constraints. This shares some commonalities with similar problems that have been extensively studied in the context of urban vehicles and it is only natural that the recent literature has extended the latter to fit aerial vehicle constraints. The knowledge of such problems, their formulation, the resolution methods proposed—through the variants induced specifically by UAVs features—are of interest for practitioners for any UAV application. Hence, in this study, we propose a review of existing literature devoted to such UAV path optimization problems, focusing specifically on the sub-class of problems that consider the mobility on a macroscopic scale. These are related to the two existing general classic ones—the Traveling Salesman Problem and the Vehicle Routing Problem. We analyze the recent literature that adapted the problems to the UAV context, provide an extensive classification and taxonomy of their problems and their formulation and also give a synthetic overview of the resolution techniques, performance metrics and obtained numerical results. Full article
(This article belongs to the Special Issue Drones Navigation and Orientation)
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