Special Issue "Application of Multiagent Systems and Artificial Intelligence Techniques in Aviation (Volume II)"

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: closed (31 December 2020).

Special Issue Editors

Dr. Alexei Sharpanskykh
E-Mail Website
Guest Editor
Aerospace Engineering Department, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, the Netherlands
Interests: artificial intelligence techniques for air transport; multiagent systems; complex sociotechnical systems; distributed planning and scheduling; airports and airlines; urban air mobility
Special Issues and Collections in MDPI journals
Dr. António J.M. Castro
E-Mail Website
Guest Editor
LIACC (Laboratory of Artificial Intelligence and Computer Science), University of Porto, 4099-002 Porto, Portugal
Interests: distributed systems; multi-agent systems in general; organization structure in distributed systems/MAS; agent oriented software engineering; intelligent user interfaces; learning (machine learning); evolutionary computing; autonomy; MAS and agents in aerospace; disruption management in airline/airport operations, space operations and air traffic control
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Methods and tools from the areas of multiagent systems (MAS) and artificial intelligence (AI) have been gaining more and more popularity in aerospace. Next to current, highly-popular Big Data and machine learning techniques stemming from statistical AI, approaches from symbolic AI, based on rules, ontologies, mathematical logics, and formal reasoning are also applied in diverse areas of aerospace, such ATM, aircraft design, airport operations, maintenance, swarming of satellites, and UAS/UAV. A new direction of multiagent organizations and agent-based modelling and simulation (ABMS) of air transport and space operations, which includes interaction between humans and technical systems, is also growing in popularity.

The techniques, methods, and tools in AI, and MAS, and ABMS in particular, advance rapidly with every passing year, thus opening up new opportunities for diverse engineering applications in airspace. AI- and MAS-based solutions have repeatedly demonstrated more robustness, flexibility, and scalability than more traditional top-down approaches. However, the full potential of these novel techniques in application to airspace is to be determined.

This Special Issue welcomes a whole range of contributions, in which AI, MAS, and ABMS techniques are developed and/or applied to aerospace.

Topics of interest include, but are not limited to:

- Autonomous agents and multiagent systems in aerospace applications
- Knowledge representation, reasoning, and logic in aerospace applications
- Agent-based modelling and simulation of sociotechnical systems in aerospace
- Robotics, perception, and vision in aerospace applications
- Big Data, machine learning, and data mining in aerospace applications
- Planning and scheduling in air transport
- Industrial aerospace applications of AI, MAS and ABMS

Dr. Alexei Sharpanskykh
Dr. António J.M. Castro
Guest Editors

Manuscript Submission Information

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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. Aerospace 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 1400 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

Article
Systemic Agent-Based Modeling and Analysis of Passenger Discretionary Activities in Airport Terminals
Aerospace 2021, 8(6), 162; https://doi.org/10.3390/aerospace8060162 - 09 Jun 2021
Viewed by 212
Abstract
Discretionary activities such as retail, food, and beverages generate a significant amount of non-aeronautical revenue within the aviation industry. However, they are rarely taken into account in computational airport terminal models. Since discretionary activities affect passenger flow and global airport terminal performance, discretionary [...] Read more.
Discretionary activities such as retail, food, and beverages generate a significant amount of non-aeronautical revenue within the aviation industry. However, they are rarely taken into account in computational airport terminal models. Since discretionary activities affect passenger flow and global airport terminal performance, discretionary activities need to be studied in detail. Additionally, discretionary activities are influenced by other airport terminal processes, such as check-in and security. Thus, discretionary activities need to be studied in relation to other airport terminal processes. The aim of this study is to analyze discretionary activities in a systemic way, taking into account interdependencies with other airport terminal processes and operational strategies used to manage these processes. An agent-based simulation model for airport terminal operations was developed, which covers the main handling processes and passenger decision-making with discretionary activities. The obtained simulation results show that operational strategies that reduce passenger queue time or increase passenger free time can significantly improve global airport terminal performance through efficiency, revenue, and cost. Full article
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Article
Machine Learning and Natural Language Processing for Prediction of Human Factors in Aviation Incident Reports
Aerospace 2021, 8(2), 47; https://doi.org/10.3390/aerospace8020047 - 11 Feb 2021
Cited by 2 | Viewed by 1238
Abstract
In the aviation sector, human factors are the primary cause of safety incidents. Intelligent prediction systems, which are capable of evaluating human state and managing risk, have been developed over the years to identify and prevent human factors. However, the lack of large [...] Read more.
In the aviation sector, human factors are the primary cause of safety incidents. Intelligent prediction systems, which are capable of evaluating human state and managing risk, have been developed over the years to identify and prevent human factors. However, the lack of large useful labelled data has often been a drawback to the development of these systems. This study presents a methodology to identify and classify human factor categories from aviation incident reports. For feature extraction, a text pre-processing and Natural Language Processing (NLP) pipeline is developed. For data modelling, semi-supervised Label Spreading (LS) and supervised Support Vector Machine (SVM) techniques are considered. Random search and Bayesian optimization methods are applied for hyper-parameter analysis and the improvement of model performance, as measured by the Micro F1 score. The best predictive models achieved a Micro F1 score of 0.900, 0.779, and 0.875, for each level of the taxonomic framework, respectively. The results of the proposed method indicate that favourable predicting performances can be achieved for the classification of human factors based on text data. Notwithstanding, a larger data set would be recommended in future research. Full article
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Article
Using Convolutional Neural Networks to Automate Aircraft Maintenance Visual Inspection
Aerospace 2020, 7(12), 171; https://doi.org/10.3390/aerospace7120171 - 07 Dec 2020
Cited by 3 | Viewed by 1243
Abstract
Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operations. Though supporting aircraft maintenance engineers detect and classify a wide [...] Read more.
Convolutional Neural Networks combined with autonomous drones are increasingly seen as enablers of partially automating the aircraft maintenance visual inspection process. Such an innovative concept can have a significant impact on aircraft operations. Though supporting aircraft maintenance engineers detect and classify a wide range of defects, the time spent on inspection can significantly be reduced. Examples of defects that can be automatically detected include aircraft dents, paint defects, cracks and holes, and lightning strike damage. Additionally, this concept could also increase the accuracy of damage detection and reduce the number of aircraft inspection incidents related to human factors like fatigue and time pressure. In our previous work, we have applied a recent Convolutional Neural Network architecture known by MASK R-CNN to detect aircraft dents. MASK-RCNN was chosen because it enables the detection of multiple objects in an image while simultaneously generating a segmentation mask for each instance. The previously obtained F1 and F2 scores were 62.67% and 59.35%, respectively. This paper extends the previous work by applying different techniques to improve and evaluate prediction performance experimentally. The approach uses include (1) Balancing the original dataset by adding images without dents; (2) Increasing data homogeneity by focusing on wing images only; (3) Exploring the potential of three augmentation techniques in improving model performance namely flipping, rotating, and blurring; and (4) using a pre-classifier in combination with MASK R-CNN. The results show that a hybrid approach combining MASK R-CNN and augmentation techniques leads to an improved performance with an F1 score of (67.50%) and F2 score of (66.37%). Full article
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Article
Utilization of FPGA for Onboard Inference of Landmark Localization in CNN-Based Spacecraft Pose Estimation
Aerospace 2020, 7(11), 159; https://doi.org/10.3390/aerospace7110159 - 05 Nov 2020
Cited by 1 | Viewed by 1272
Abstract
In the recent past, research on the utilization of deep learning algorithms for space applications has been widespread. One of the areas where such algorithms are gaining attention is in spacecraft pose estimation, which is a fundamental requirement in many spacecraft rendezvous and [...] Read more.
In the recent past, research on the utilization of deep learning algorithms for space applications has been widespread. One of the areas where such algorithms are gaining attention is in spacecraft pose estimation, which is a fundamental requirement in many spacecraft rendezvous and navigation operations. Nevertheless, the application of such algorithms in space operations faces unique challenges compared to application in terrestrial operations. In the latter, they are facilitated by powerful computers, servers, and shared resources, such as cloud services. However, these resources are limited in space environment and spacecrafts. Hence, to take advantage of these algorithms, an on-board inferencing that is power- and cost-effective is required. This paper investigates the use of a hybrid Field Programmable Gate Array (FPGA) and Systems-on-Chip (SoC) device for efficient onboard inferencing of the Convolutional Neural Network (CNN) part of such pose estimation methods. In this study, Xilinx’s Zynq UltraScale+ MPSoC device is used and proposed as an effective onboard-inferencing solution. The performance of the onboard and computer inferencing is compared, and the effectiveness of the hybrid FPGA-CPU architecture is verified. The FPGA-based inference has comparable accuracy to the PC-based inference with an average RMS error difference of less than 0.55. Two CNN models that are based on encoder-decoder architecture have been investigated in this study and three approaches demonstrated for landmarks localization. Full article
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Article
Data-Driven Analysis of Airport Security Checkpoint Operations
Aerospace 2020, 7(6), 69; https://doi.org/10.3390/aerospace7060069 - 29 May 2020
Cited by 1 | Viewed by 1701
Abstract
Airport security checkpoints are the most important bottleneck in airport operations, but few studies aim to empirically understand them better. In this work we address this lack of data-driven quantitative analysis and insights about the security checkpoint process. To this end, we followed [...] Read more.
Airport security checkpoints are the most important bottleneck in airport operations, but few studies aim to empirically understand them better. In this work we address this lack of data-driven quantitative analysis and insights about the security checkpoint process. To this end, we followed a total of 2277 passengers through the security checkpoint process at Rotterdam The Hague Airport (RTM), and published detailed timing data about their journey through the process. This dataset is unique in scientific literature, and can aid future researchers in the modelling and analysis of the security checkpoint. Our analysis showed important differences between six identified passenger types. Business passengers were found to be the fastest group, while passengers with reduced mobility (PRM) and families were the slowest two groups. We also identified events that hindered the performance of the security checkpoint, in which groups of passengers had to wait long for security employees or other passengers. A total of 335 such events occurred, with an average of 2.3 passengers affected per event. It was found that a passenger that had a high luggage drop time was followed by an event in 27% of the cases, which was the most frequent cause. To mitigate this waiting time of subsequent passengers in the security checkpoint process, we performed an experiment with a so-called service lane. This lane was used to process passengers that are expected to be slow, while the remaining lanes processed the other passengers. It was found that the mean throughput of the service lane setups was higher than the average throughput of the standard lanes, making it a promising setup to investigate further. Full article
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Article
Agent-Based Distributed Planning and Coordination for Resilient Airport Surface Movement Operations
Aerospace 2020, 7(4), 48; https://doi.org/10.3390/aerospace7040048 - 19 Apr 2020
Viewed by 2361
Abstract
Airport surface movement operations are complex processes with many types of adverse events which require resilient, safe, and efficient responses. One regularly occurring adverse event is that of runway reconfiguration. Agent-based distributed planning and coordination has shown promising results in controlling operations in [...] Read more.
Airport surface movement operations are complex processes with many types of adverse events which require resilient, safe, and efficient responses. One regularly occurring adverse event is that of runway reconfiguration. Agent-based distributed planning and coordination has shown promising results in controlling operations in complex systems, especially during disturbances. In contrast to the centralised approaches, distributed planning is performed by several agents, which coordinate plans with each other. This research evaluates the contribution of agent-based distributed planning and coordination to the resilience of airport surface movement operations when runway reconfigurations occur. An autonomous Multi-Agent System (MAS) model was created based on the layout and airport surface movement operations of Schiphol Airport in the Netherlands. Within the MAS model, three distributed planning and coordination mechanisms were incorporated, based on the Conflict-Based Search (CBS) Multi-Agent Path Finding (MAPF) algorithm and adaptive highways. MAS simulations were run based on eight days of real-world operational data from Schiphol Airport and the results of the autonomous MAS simulations were compared to the performance of the real-world human operated system. The MAS results show that the distributed planning and coordination mechanisms were effective in contributing to the resilient behaviour of the airport surface movement operations, closely following the real-world behaviour, and sometimes even surpassing it. In particular, the mechanisms were found to contribute to more resilient behaviour than the real-world when considering the taxi time after runway reconfiguration events. Finally, the highway included distributed planning and coordination mechanisms contributed to the most resilient behaviour of the airport surface movement operations. Full article
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