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Moving Object Detection and Control Using Remote Sensing and Artificial Intelligence II

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 2159

Special Issue Editors


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Guest Editor
Department of Ship Automation, Faculty of Marine Electrical Engineering, Gdynia Maritime University, 83 Morska Str., 81-225 Gdynia, Poland
Interests: control engineering; optimization; differential games; artificial intelligence; sensitivity of control; remote sensing; technology development; applications
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Department of Ship Automation, Faculty of Marine Electrical Engineering, Gdynia Maritime University, 83 Morska Str., 81-225 Gdynia, Poland
Interests: engineering; computer science; automation and control systems; transportation; robotics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Moving objects constitute a significant part of all technical objects, for which the method of controlling their movement significantly affects both operating costs and the accuracy as well as safety of transport tasks. This applies to land, sea, and air objects in terms of manned and unmanned facilities. Remote sensing devices, such as radar, lidar, and other highly specialized measurement solutions, are used in the detection as well as control of moving objects. When planning and implementing the motion control of objects, there are many possible acceptable solutions from which the best or optimal solution should be selected. To find it, both static and dynamic optimization deterministic methods, in addition to heuristic methods of particle swarms, are used, as are very effective methods of artificial intelligence in the form of evolutionary algorithms and neuro-fuzzy regulators. The topics of interest also include other AI approaches applied for the detection, path planning, and motion control of various moving objects, such as autonomous vehicles, self-driving cars, aircrafts, and ships, based on machine learning, neural networks as well as deep learning, fuzzy logic, and multiagent as well as expert systems.

In addition, moving objects are often affected by various types of interference, which are compensated for via adaptive algorithms using the following methods: self-tuning, a model reference system, or gain scheduling.

When carrying out transport tasks, there are situations of passing by many other objects. In such situations, the subjectivity of the operator of an object in assessing the navigational situation is important. They must take into account the applicable legal rules in addition to the possibility of making a mistake and contributing to a collision situation, which can be considered as a conflict situation. Game theory, which is a branch of modern mathematics, including the theory of conflict situations and the construction as well as analysis of their models, comes to the rescue. Therefore, it is appropriate to treat the process of passing objects safely as a game, taking into account the cooperation or non-cooperation between objects.

For this Special Issue, we seek innovative approaches that use remote sensing and control to develop appropriate algorithms of computer-aided maneuvering decisions, calculating all possible solutions of the task and proposing one of the best ones.

We welcome review papers, case studies, computer simulations, technology developments, and applications.

You may choose our Joint Special Issue in Sensors.

Prof. Dr. Józef Lisowski
Dr. Agnieszka Lazarowska
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. Remote Sensing 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 2700 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

  • land, sea, and aerial moving objects
  • manned and unmanned objects
  • remote sensing
  • artificial intelligence
  • control engineering
  • multicriteria optimization
  • adaptive control
  • game theory application
  • sensitivity of control
  • machine learning
  • neural networks and deep learning
  • fuzzy logic
  • multiagent systems
  • expert systems
  • autonomous vehicles
  • swarm intelligence

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Published Papers (1 paper)

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Research

20 pages, 8678 KiB  
Article
Vision-Based Mid-Air Object Detection and Avoidance Approach for Small Unmanned Aerial Vehicles with Deep Learning and Risk Assessment
by Ying-Chih Lai and Tzu-Yun Lin
Remote Sens. 2024, 16(5), 756; https://doi.org/10.3390/rs16050756 - 21 Feb 2024
Cited by 2 | Viewed by 1589
Abstract
With the increasing demand for unmanned aerial vehicles (UAVs), the number of UAVs in the airspace and the risk of mid-air collisions caused by UAVs are increasing. Therefore, detect and avoid (DAA) technology for UAVs has become a crucial element for mid-air collision [...] Read more.
With the increasing demand for unmanned aerial vehicles (UAVs), the number of UAVs in the airspace and the risk of mid-air collisions caused by UAVs are increasing. Therefore, detect and avoid (DAA) technology for UAVs has become a crucial element for mid-air collision avoidance. This study presents a collision avoidance approach for UAVs equipped with a monocular camera to detect small fixed-wing intruders. The proposed system can detect any size of UAV over a long range. The development process consists of three phases: long-distance object detection, object region estimation, and collision risk assessment and collision avoidance. For long-distance object detection, an optical flow-based background subtraction method is utilized to detect an intruder far away from the host. A mask region-based convolutional neural network (Mask R-CNN) model is trained to estimate the region of the intruder in the image. Finally, the collision risk assessment adopts the area expansion rate and bearing angle of the intruder in the images to conduct mid-air collision avoidance based on visual flight rules (VFRs) and conflict areas. The proposed collision avoidance approach is verified by both simulations and experiments. The results show that the system can successfully detect different sizes of fixed-wing intruders, estimate their regions, and assess the risk of collision at least 10 s in advance before the expected collision would happen. Full article
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