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Tracking and Sensing Based on Autonomous Aerial Vehicles
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Tracking and Sensing Based on Autonomous Aerial Vehicles

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 7470

Special Issue Editors

Dr. Leszek Ambroziak

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Guest Editor
Department of Robotics and Mechatronics, Faculty of Mechanical Engineering, Bialystok University of Technology, Wiejska St. 45C, 15-351 Bialystok, Poland
Interests: robotics; mobile robotics; robot cooperation; autonomous agents; machine learning; computer vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ewa Pawłuszewicz

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, Bialystok University of Technology, 15-351 Białystok, Poland
Interests: nonlinear control systems; realizability, reducibility of control systems; fractional systems; application of fractional tools to industrial process control; control systems on time scales
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Krzysztof Sibilski

E-Mail Website
Guest Editor
Faculty of Power and Aeronautical Engineering, Institute of Aeronautics and Applied Mechanics, Doctoral School No. 4, Warsaw University of Technology, 00-665 Warsaw, Poland
Interests: Flight Mechanics; Navigation and Guidance; Aerodynamics
Dr. Ashutosh Simha

E-Mail Website
Guest Editor
Embedded and Networked Systems Group, Delft University of Technology, 2628 CD Delft, The Netherlands
Interests: Control Systems; Geometric Control Theory; Unmanned Aerial Vehicles; Underwater Robots

Special Issue Information

Dear Colleagues,

We are inviting submissions to a Special Issue of Sensors on the subject area encapsulated in the title, “Tracking and Sensing Based on Autonomous Aerial Vehicles”. Unmanned aerial vehicles (UAVs) are currently being researched for a wide range of military and civil applications, such as surveillance and reconnaissance purposes, aerial surveys for agriculture, traffic monitoring, pollution control, meteorological data collection, pipeline and electrical transmission line survey, early fire detection, wildlife population tracking, crowd monitoring, actions against poaching, and more. In all of these applications, appropriate communication, control, navigation, tracking, and sensing onboard systems play a prominent role and determine the success of the mission regardless of the type of flying robot (fixed wing, multirotor, hybrid VTOLs). Tracking and sensing operations based on UAVs must be preceded by a thorough analysis, low level control and navigation synthesis, careful simulation studies, and precise preparation of hardware for in-flight studies. UAVs applied to any task must be carefully tested in flight and in a number of extreme scenarios before being used in any applications. This Special Issue is focused on new developments in the field of control, navigation, tracking, and sensing based on UAVs being used for various applications – civil and military

Dr. Leszek Ambroziak
Prof. Dr. Ewa Pawłuszewicz
Prof. Dr. Krzysztof Sibilski
Dr. Ashutosh Simha
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

  • unmanned aerial vehicles
  • navigation
  • path planning
  • collision avoidance
  • formation flying
  • swarming
  • remote sensing
  • intelligent flying sensors

Published Papers (4 papers)

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Research

19 pages, 6254 KiB  
Article
Influence of Insufficient Dataset Augmentation on IoU and Detection Threshold in CNN Training for Object Detection on Aerial Images
by Arkadiusz Bożko and Leszek Ambroziak
Sensors 2022, 22(23), 9080; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239080 - 23 Nov 2022
Cited by 2 | Viewed by 1586
Abstract
The objects and events detection tasks are being performed progressively often by robotic systems like unmanned aerial vehicles (UAV) or unmanned surface vehicles (USV). Autonomous operations and intelligent sensing are becoming standard in numerous scenarios such as supervision or even search and rescue [...] Read more.
The objects and events detection tasks are being performed progressively often by robotic systems like unmanned aerial vehicles (UAV) or unmanned surface vehicles (USV). Autonomous operations and intelligent sensing are becoming standard in numerous scenarios such as supervision or even search and rescue (SAR) missions. The low cost of autonomous vehicles, vision sensors and portable computers allows the incorporation of the deep learning, mainly convolutional neural networks (CNN) in these solutions. Many systems meant for custom purposes rely on insufficient training datasets, what may cause a decrease of effectiveness. Moreover, the system’s accuracy is usually dependent on the returned bounding boxes highlighting the supposed targets. In desktop applications, precise localisation might not be particularly relevant; however, in real situations, with low visibility and non-optimal camera orientation, it becomes crucial. One of the solutions for dataset enhancement is its augmentation. The presented work is an attempt to evaluate the influence of the training images augmentation on the detection parameters important for the effectiveness of neural networks in the context of object detection. In this research, network appraisal relies on the detection confidence and bounding box prediction accuracy (IoU). All the applied image modifications were simple pattern and colour alterations. The obtained results imply that there is a measurable impact of the augmentation process on the localisation accuracy. It was concluded that a positive or negative influence is related to the complexity and variability of the objects classes. Full article
(This article belongs to the Special Issue Tracking and Sensing Based on Autonomous Aerial Vehicles)
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18 pages, 32412 KiB  
Article
Real-Time Ship Segmentation in Maritime Surveillance Videos Using Automatically Annotated Synthetic Datasets
by Miguel Ribeiro, Bruno Damas and Alexandre Bernardino
Sensors 2022, 22(21), 8090; https://0-doi-org.brum.beds.ac.uk/10.3390/s22218090 - 22 Oct 2022
Cited by 10 | Viewed by 2373
Abstract
This work proposes a new system capable of real-time ship instance segmentation during maritime surveillance missions by unmanned aerial vehicles using an onboard standard RGB camera. The implementation requires two stages: an instance segmentation network able to produce fast and reliable preliminary segmentation [...] Read more.
This work proposes a new system capable of real-time ship instance segmentation during maritime surveillance missions by unmanned aerial vehicles using an onboard standard RGB camera. The implementation requires two stages: an instance segmentation network able to produce fast and reliable preliminary segmentation results and a post-processing 3D fully connected Conditional Random Field, which significantly improves segmentation results by exploring temporal correlations between nearby frames in video sequences. Moreover, due to the absence of maritime datasets consisting of properly labeled video sequences, we create a new dataset comprising synthetic video sequences of maritime surveillance scenarios (MarSyn). The main advantages of this approach are the possibility of generating a vast set of images and videos, being able to represent real-world scenarios without the necessity of deploying the real vehicle, and automatic labels, which eliminate human labeling errors. We train the system with the MarSyn dataset and with aerial footage from publicly available annotated maritime datasets to validate the proposed approach. We present some experimental results and compare them to other approaches, and we also illustrate the temporal stability provided by the second stage in missing frames and wrong segmentation scenarios. Full article
(This article belongs to the Special Issue Tracking and Sensing Based on Autonomous Aerial Vehicles)
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17 pages, 8392 KiB  
Article
Asymmetrical Artificial Potential Field as Framework of Nonlinear PID Loop to Control Position Tracking by Nonholonomic UAVs
by Cezary Kownacki and Leszek Ambroziak
Sensors 2022, 22(15), 5474; https://0-doi-org.brum.beds.ac.uk/10.3390/s22155474 - 22 Jul 2022
Cited by 1 | Viewed by 1057
Abstract
Precise position tracking plays a key role in formation flights of UAVs (unmanned aerial vehicles) or other applications based on the idea of the leader–following scheme. It decides on the integrity of a formation or increasing the position error when a UAV follows [...] Read more.
Precise position tracking plays a key role in formation flights of UAVs (unmanned aerial vehicles) or other applications based on the idea of the leader–following scheme. It decides on the integrity of a formation or increasing the position error when a UAV follows the desired flight path. This is especially difficult in the case of nonholonomic vehicles having limited possibilities of making turns, causing a lack of stability. An asymmetrical artificial potential field (AAPF) is the way to achieve the stability of position tracking by nonholonomic UAVs, but it is only a nonlinear proportional relation to feedback given by a tracking error. Therefore, there can still be a steady-state error or error overshoots. Combining an AAPF with integral and derivative terms can improve the response of control by damping overshoots and minimizing the steady-state error. Such a combination results in a regulator whose properties allow defining it as nonlinear PID. Numerical simulation confirms that integral and derivative terms together with an AAPF create a control loop that can minimize overshoots of the tracking error and the steady-state error and satisfy conditions of asymptotical stability. Full article
(This article belongs to the Special Issue Tracking and Sensing Based on Autonomous Aerial Vehicles)
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22 pages, 17415 KiB  
Article
A New Multidimensional Repulsive Potential Field to Avoid Obstacles by Nonholonomic UAVs in Dynamic Environments
by Cezary Kownacki and Leszek Ambroziak
Sensors 2021, 21(22), 7495; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227495 - 11 Nov 2021
Cited by 5 | Viewed by 1733
Abstract
The ability of autonomous flight with obstacle avoidance should be a fundamental feature of all modern unmanned aerial vehicles (UAVs). The complexity and difficulty of such a task, however, significantly increase in cases combining moving obstacles and nonholonomic UAVs. Additionally, since they assume [...] Read more.
The ability of autonomous flight with obstacle avoidance should be a fundamental feature of all modern unmanned aerial vehicles (UAVs). The complexity and difficulty of such a task, however, significantly increase in cases combining moving obstacles and nonholonomic UAVs. Additionally, since they assume the symmetrical distribution of repulsive forces around obstacles, traditional repulsive potential fields are not well suited for nonholonomic vehicles. The limited maneuverability of these types of UAVs, including fixed-wing aircraft, requires consideration not only of their relative position, but also their speed as well as the direction in which the obstacles are moving. To address this issue, the following work presents a novel multidimensional repulsive potential field dedicated to nonholonomic UAVs. This field generates forces that repulse the UAV not from the obstacle’s geometrical center, but from areas immediately behind and in front of it located along a line defined by the obstacle’s velocity vector. The strength of the repulsive force depends on the UAV’s distance to the line representing the obstacle’s movement direction, distance to the obstacle along that line, and the relative speed between the UAV and the obstacle projected to the line, making the proposed repulsive potential field multidimensional. Numerical simulations presented within the paper prove the effectiveness of the proposed novel repulsive potential field in controlling the flight of nonholonomic UAVs. Full article
(This article belongs to the Special Issue Tracking and Sensing Based on Autonomous Aerial Vehicles)
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