Special Issue "Advances in AI for Intelligent Autonomous Systems"

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

Deadline for manuscript submissions: 30 November 2023 | Viewed by 815

Special Issue Editors

Netherlands Organisation for Applied Scientific Research, Den Haag, The Netherlands
Interests: Artificial Intelligence; self-organization; nature-inspired optimization; emergent behavior; logic; intelligent autonomous systems; UAVs; planning; scheduling
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 2000 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

Published Papers (1 paper)

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Research

Article
Anomaly Detection for Data from Unmanned Systems via Improved Graph Neural Networks with Attention Mechanism
Drones 2023, 7(5), 326; https://doi.org/10.3390/drones7050326 - 19 May 2023
Viewed by 391
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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