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Drones
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Advances in AI for Intelligent Autonomous Systems
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Advances in AI for Intelligent Autonomous Systems

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

Deadline for manuscript submissions: closed (20 May 2024) | Viewed by 14683

Special Issue Editors

Dr. Hanno Hildmann

E-Mail Website
Guest Editor
1. IAS Intelligent Autonomous Systems, Netherlands Organisation for Applied Scientific Research (TNO), 2595 DA The Hague, The Netherlands
2. De Haagse Hogeschool, The Hague University of Applied Sciences (HHS), 2521 EN Den Haag, The Netherlands
Interests: artificial intelligence; swarm intelligence; swarming; self-organization; nature-inspired optimization; emergent behavior; logic; intelligent autonomous systems; UAVs; planning; scheduling
Prof. Dr. Fabrice Saffre

E-Mail Website
Guest Editor
Technical Research Centre of Finland, 02150 Espoo, Finland
Interests: distributed systems; cloud computing; computational intelligence; distribution; environment; distributed computing; computer networks; network; Artificial Intelligence; drones

Special Issue Information

Dear Colleagues,

As unmanned aerial vehicles become an increasingly mainstream technology and their utilisation more ubiquitous, the opportunities afforded by their becoming more autonomous and “intelligent” multiply. There is a wide variety of applications in which an ability for drones to operate semi-independently from a human pilot could bring substantial advantages. Autonomous decision-making based on information collected in real time and the ability for several drones to communicate and cooperate in the execution of a complex mission would open countless new possibilities.

For this Special Issue, we invite original contributions presenting advances in Artificial Intelligence (machine learning, visual pattern recognition, swarm intelligence, etc.) that significantly increase the autonomy of unmanned systems, whether operating individually or as a team. Rigorous theoretical or simulation-based studies, prototype implementation reports, and experimental results are equally welcome, as are comprehensive literature surveys and reviews. Innovative research on the interaction between humans and “intelligent” drones (interface design, concepts of operation, etc.) or the control of autonomous platforms is also of interest.

Dr. Hanno Hildmann
Prof. Dr. Fabrice Saffre
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.

Keywords

  • Artificial Intelligence
  • autonomy
  • swarm behaviour
  • co-ordination
  • communication
  • navigation
  • interaction
  • decision making
  • self-organisation
  • spatial organization

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Published Papers (5 papers)

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Research

Jump to: Review

23 pages, 1525 KiB  
Article
Path Planning for Fixed-Wing Unmanned Aerial Vehicles: An Integrated Approach with Theta* and Clothoids
by Salvatore Rosario Bassolillo, Gennaro Raspaolo, Luciano Blasi, Egidio D’Amato and Immacolata Notaro
Drones 2024, 8(2), 62; https://doi.org/10.3390/drones8020062 - 12 Feb 2024
Cited by 3 | Viewed by 2902
Abstract
Unmanned Aerial Vehicles (UAVs) have emerged as a compelling alternative to manned operations, offering the capability to navigate hazardous environments without risks for human operators. Despite their potential, optimizing UAV missions in complex and unstructured environments remains a pivotal challenge. Path planning becomes [...] Read more.
Unmanned Aerial Vehicles (UAVs) have emerged as a compelling alternative to manned operations, offering the capability to navigate hazardous environments without risks for human operators. Despite their potential, optimizing UAV missions in complex and unstructured environments remains a pivotal challenge. Path planning becomes a crucial aspect to increase mission efficiency, although it is inherently complex due to various factors such as obstacles, no-fly zones, non-cooperative aircraft, and flight mechanics limitations. This paper presents a path-planning technique for fixed-wing unmanned aerial vehicles (UAVs) based on the Theta* algorithm. The approach introduces innovative features, such as the use of Euler spiral, or clothoids, to serve as connection arcs between nodes, mitigating trajectory discontinuities. The design of clothoids can be linked to the aircraft performance model, establishing a connection between curvature constraints and the specific characteristics of the vehicle. Furthermore, to lower the computational burden, the implementation of an adaptive exploration distance and a vision cone was considered, reducing the number of explored solutions. This methodology ensures a seamless and optimized flight path for fixed-wing UAVs operating in static environments, showcasing a noteworthy improvement in trajectory smoothness. The proposed methodology has been numerically evaluated in several complex test cases as well as in a real urban scenario to prove its effectiveness. Full article
(This article belongs to the Special Issue Advances in AI for Intelligent Autonomous Systems)
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21 pages, 1546 KiB  
Article
Information Transmission in a Drone Swarm: A Temporal Network Analysis
by Patrick Grosfils
Drones 2024, 8(1), 28; https://doi.org/10.3390/drones8010028 - 21 Jan 2024
Cited by 2 | Viewed by 1724
Abstract
We consider an ensemble of drones moving in a two-dimensional domain, each one of them carrying a communication device, and we investigate the problem of information transfer in the swarm when the transmission capabilities are short range. The problem is discussed under the [...] Read more.
We consider an ensemble of drones moving in a two-dimensional domain, each one of them carrying a communication device, and we investigate the problem of information transfer in the swarm when the transmission capabilities are short range. The problem is discussed under the framework of temporal networks, and special attention is paid to the analysis of the transmission time of messages transported within the swarm. Traditional theoretical methods of graph theory are extended to tackle the problem of time-varying networks and a numerical analysis of the detection time statistics is performed in order to evaluate the efficiency of the communication network as a function of the parameters characterizing the swarm dynamics. Full article
(This article belongs to the Special Issue Advances in AI for Intelligent Autonomous Systems)
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18 pages, 20818 KiB  
Article
A Visual Odometry Pipeline for Real-Time UAS Geopositioning
by Jianli Wei and Alper Yilmaz
Drones 2023, 7(9), 569; https://doi.org/10.3390/drones7090569 - 5 Sep 2023
Cited by 2 | Viewed by 2043
Abstract
The state-of-the-art geopositioning is the Global Navigation Satellite System (GNSS), which operates based on the satellite constellation providing positioning, navigation, and timing services. While the Global Positioning System (GPS) is widely used to position an Unmanned Aerial System (UAS), it is not always [...] Read more.
The state-of-the-art geopositioning is the Global Navigation Satellite System (GNSS), which operates based on the satellite constellation providing positioning, navigation, and timing services. While the Global Positioning System (GPS) is widely used to position an Unmanned Aerial System (UAS), it is not always available and can be jammed, introducing operational liabilities. When the GPS signal is degraded or denied, the UAS navigation solution cannot rely on incorrect positions GPS provides, resulting in potential loss of control. This paper presents a real-time pipeline for geopositioning functionality using a down-facing monocular camera. The proposed approach is deployable using only a few initialization parameters, the most important of which is the map of the area covered by the UAS flight plan. Our pipeline consists of an offline geospatial quad-tree generation for fast information retrieval, a choice from a selection of landmark detection and matching schemes, and an attitude control mechanism that improves reference to acquired image matching. To evaluate our method, we collected several image sequences using various flight patterns with seasonal changes. The experiments demonstrate high accuracy and robustness to seasonal changes. Full article
(This article belongs to the Special Issue Advances in AI for Intelligent Autonomous Systems)
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24 pages, 3480 KiB  
Article
Anomaly Detection for Data from Unmanned Systems via Improved Graph Neural Networks with Attention Mechanism
by Guoying Wang, Jiafeng Ai, Lufeng Mo, Xiaomei Yi, Peng Wu, Xiaoping Wu and Linjun Kong
Drones 2023, 7(5), 326; https://doi.org/10.3390/drones7050326 - 19 May 2023
Cited by 9 | Viewed by 3425
Abstract
Anomaly detection has an important impact on the development of unmanned aerial vehicles, and effective anomaly detection is fundamental to their utilization. Traditional anomaly detection discriminates anomalies for single-dimensional factors of sensing data, which often performs poorly in multidimensional data scenarios due to [...] Read more.
Anomaly detection has an important impact on the development of unmanned aerial vehicles, and effective anomaly detection is fundamental to their utilization. Traditional anomaly detection discriminates anomalies for single-dimensional factors of sensing data, which often performs poorly in multidimensional data scenarios due to weak computational scalability and the problem of dimensional catastrophe, ignoring potential correlations between sensing data and some important information of certain characteristics. In order to capture the correlation of multidimensional sensing data and improve the accuracy of anomaly detection effectively, GTAF, an anomaly detection model for multivariate sequences based on an improved graph neural network with a transformer, a graph attention mechanism and a multi-channel fusion mechanism, is proposed in this paper. First, we added a multi-channel transformer structure for intrinsic pattern extraction of different data. Then, we combined the multi-channel transformer structure with GDN’s original graph attention network (GAT) to attain better capture of features of time series, better learning of dependencies between time series and hence prediction of future values of adjacent time series. Finally, we added a multi-channel data fusion module, which utilizes channel attention to integrate global information and upgrade anomaly detection accuracy. The results of experiments show that the average accuracies of GTAF, the anomaly detection model proposed in this paper, are 92.83% and 96.59% on two datasets from unmanned systems, respectively, which has higher accuracy and computational efficiency compared with other methods. Full article
(This article belongs to the Special Issue Advances in AI for Intelligent Autonomous Systems)
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Review

Jump to: Research

36 pages, 1849 KiB  
Review
The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV
by Dušan Herich and Ján Vaščák
Drones 2024, 8(2), 34; https://doi.org/10.3390/drones8020034 - 24 Jan 2024
Cited by 2 | Viewed by 3001
Abstract
The Internet of Vehicles (IoV) and the Internet of Flying Vehicles (IoFV) are integral components of intelligent transportation systems with the potential to revolutionize the way we move people and goods. Although both the IoV and IoFV share a common goal of improving [...] Read more.
The Internet of Vehicles (IoV) and the Internet of Flying Vehicles (IoFV) are integral components of intelligent transportation systems with the potential to revolutionize the way we move people and goods. Although both the IoV and IoFV share a common goal of improving transportation efficiency, safety, and sustainability, they possess distinct characteristics and face unique challenges. To date, the existing literature has predominantly focused on specific aspects of either the IoV or IoFV, but a comprehensive review comparing and contrasting the two domains is still lacking. This review paper aims to address this gap by providing an in-depth analysis of the key differences between the IoV and IoFV systems. The review will examine the technological components, network infrastructure, communication protocols, data management, objectives, applications, challenges, and future trends associated with both domains. Additionally, this paper will explore the potential impact of technologies such as artificial intelligence, machine learning, and blockchain. Ultimately, the paper aims to contribute to a deeper understanding of the implications and potential of these technologies, both in the context of transportation systems and beyond. Full article
(This article belongs to the Special Issue Advances in AI for Intelligent Autonomous Systems)
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