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Aerospace
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Artificial Intelligence in Drone Applications
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Artificial Intelligence in Drone Applications

A special issue of Aerospace (ISSN 2226-4310). This special issue belongs to the section "Aeronautics".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 16419

Special Issue Editors

Dr. Chao-Yang Lee

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Douliou 640301, Taiwan
Interests: artificial intelligence; Internet of Things; wireless communication networks; unmanned aerial vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Ang-Hsun Tsai

E-Mail Website
Guest Editor
Department of Communications Engineering, Feng Chia University, Taichung, Taiwan
Interests: artificial intelligence; aerial communication networks; 6G mobile communication network; radio resource management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, advanced artificial intelligence technologies have provided the opportunity for new drone applications, such as the surveillance, search and rescue, remote sensing, air pollution monitoring, precision agriculture, and aerial base stations. Therefore, there is a significant interest in the use of deep learning for various applications. However, artificial-intelligence-based methods are data-hungry and require a certain amount of meaningful available data to generate useful results. There, these methods are challenging to implement in drones due to limited resources. Thus, it is an urgent need to develop more advanced methods.

This Special Issue intends to publish original research and review articles that discuss theoretical and practical results in relation to artificial intelligence in drones, with a particular focus on navigation, perception, wireless communication, decisions, control, and civil applications using artificial intelligence technologies.

This Special Issue addresses a broad list of topics related to artificial intelligence in drones. We welcome papers that focus on, but are not limited to, the following topics:

  • Artificial intelligence in drones;
  • Machine learning in drones;
  • Deep learning in drones;
  • Reinforcement learning in drones;
  • Computer vision for the perception, navigation and control of drones;
  • Artificial-intelligence-based flight and exploration of drones;
  • Artificial-intelligence-based wireless communication for drones;
  • Artificial-intelligence-based control schemes for drones;
  • Artificial-intelligence-based path planning drones;
  • Artificial-intelligence-based obstacle avoidance for drones;
  • Artificial-intelligence-based applications in drones.

Dr. Chao-Yang Lee
Dr. Ang-Hsun Tsai
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. 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 2400 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 (7 papers)

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Research

17 pages, 1760 KiB  
Article
Hybrid Turbo-Shaft Engine Digital Twinning for Autonomous Aircraft via AI and Synthetic Data Generation
by Ali Aghazadeh Ardebili, Antonio Ficarella, Antonella Longo, Adem Khalil and Sabri Khalil
Aerospace 2023, 10(8), 683; https://doi.org/10.3390/aerospace10080683 - 31 Jul 2023
Cited by 1 | Viewed by 1984
Abstract
Autonomous aircraft are the key enablers of future urban services, such as postal and transportation systems. Digital twins (DTs) are promising cutting-edge technologies that can transform the future transport ecosystem into an autonomous and resilient system. However, since DT is a data-driven solution [...] Read more.
Autonomous aircraft are the key enablers of future urban services, such as postal and transportation systems. Digital twins (DTs) are promising cutting-edge technologies that can transform the future transport ecosystem into an autonomous and resilient system. However, since DT is a data-driven solution based on AI, proper data management is essential in implementing DT as a service (DTaaS). One of the challenges in DT development is the availability of real-life data, particularly for training algorithms and verifying the functionality of DT. The current article focuses on data augmentation through synthetic data generation. This approach can facilitate the development of DT in case the developers do not have enough data to train the machine learning (ML) algorithm. The current twinning approach provides a prospective ideal state of the engine used for proactive monitoring of the engine’s health as an anomaly detection service. In line with the track of unmanned aircraft vehicles (UAVs) for urban air mobility in smart city applications, this paper focuses specifically on the common hybrid turbo-shaft in drones/helicopters. However, there is a significant gap in real-life similar synthetic data generation in the UAV domain literature. Therefore, rolling linear regression and Kalman filter algorithms were implemented on noise-added data, which simulate the data measured from the engine in a real-life operational life cycle. For both thermal and hybrid models, the corresponding DT model has shown high efficiency in noise filtration and a certain amount of predictions with a lower error rate on all engine parameters except the engine torque. Full article
(This article belongs to the Special Issue Artificial Intelligence in Drone Applications)
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16 pages, 22707 KiB  
Article
Synthetic Training Data for Semantic Segmentation of the Environment from UAV Perspective
by Christoph Hinniger and Joachim Rüter
Aerospace 2023, 10(7), 604; https://doi.org/10.3390/aerospace10070604 - 30 Jun 2023
Cited by 2 | Viewed by 1231
Abstract
Autonomous unmanned aircraft need a good semantic understanding of their surroundings to plan safe routes or to find safe landing sites, for example, by means of a semantic segmentation of an image stream. Currently, Neural Networks often give state-of-the-art results on semantic segmentation [...] Read more.
Autonomous unmanned aircraft need a good semantic understanding of their surroundings to plan safe routes or to find safe landing sites, for example, by means of a semantic segmentation of an image stream. Currently, Neural Networks often give state-of-the-art results on semantic segmentation tasks but need a huge amount of diverse training data to achieve these results. In aviation, this amount of data is hard to acquire but the usage of synthetic data from game engines could solve this problem. However, related work, e.g., in the automotive sector, shows a performance drop when applying these models to real images. In this work, the usage of synthetic training data for semantic segmentation of the environment from a UAV perspective is investigated. A real image dataset from a UAV perspective is stylistically replicated in a game engine and images are extracted to train a Neural Network. The evaluation is carried out on real images and shows that training on synthetic images alone is not sufficient but that when fine-tuning the model, they can reduce the amount of real data needed for training significantly. This research shows that synthetic images may be a promising direction to bring Neural Networks for environment perception into aerospace applications. Full article
(This article belongs to the Special Issue Artificial Intelligence in Drone Applications)
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22 pages, 9890 KiB  
Article
Multi-UAV Cooperative Air Combat Decision-Making Based on Multi-Agent Double-Soft Actor-Critic
by Shaowei Li, Yongchao Wang, Yaoming Zhou, Yuhong Jia, Hanyue Shi, Fan Yang and Chaoyue Zhang
Aerospace 2023, 10(7), 574; https://doi.org/10.3390/aerospace10070574 - 21 Jun 2023
Cited by 2 | Viewed by 1919
Abstract
Multiple unmanned aerial vehicle (multi-UAV) cooperative air combat, which is an important form of future air combat, has high requirements for the autonomy and cooperation of unmanned aerial vehicles. Therefore, it is of great significance to study the decision-making method of multi-UAV cooperative [...] Read more.
Multiple unmanned aerial vehicle (multi-UAV) cooperative air combat, which is an important form of future air combat, has high requirements for the autonomy and cooperation of unmanned aerial vehicles. Therefore, it is of great significance to study the decision-making method of multi-UAV cooperative air combat since the conventional methods are challenging to solve the high complexity and highly dynamic cooperative air combat problems. This paper proposes a multi-agent double-soft actor-critic (MADSAC) algorithm for solving the cooperative decision-making problem of multi-UAV. The MADSAC achieves multi-UAV cooperative air combat by treating the problem as a fully cooperative game using a decentralized partially observable Markov decision process and a centrally trained distributed execution framework. The use of maximum entropy theory in the update process makes the method more exploratory. Meanwhile, MADSAC uses double-centralized critics, target networks, and delayed policy updates to solve the overestimation and error accumulation problems effectively. In addition, the double-centralized critics based on the attention mechanism improve the scalability and learning efficiency of MADSAC. Finally, multi-UAV cooperative air combat experiments validate the effectiveness of MADSAC. Full article
(This article belongs to the Special Issue Artificial Intelligence in Drone Applications)
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29 pages, 15699 KiB  
Article
Design and Simulation of a Neuroevolutionary Controller for a Quadcopter Drone
by Manuel Mariani and Simone Fiori
Aerospace 2023, 10(5), 418; https://doi.org/10.3390/aerospace10050418 - 29 Apr 2023
Cited by 4 | Viewed by 1660
Abstract
The problem addressed in the present paper is the design of a controller based on an evolutionary neural network for autonomous flight in quadrotor systems. The controller’s objective is to govern the quadcopter in such a way that it reaches a specific position, [...] Read more.
The problem addressed in the present paper is the design of a controller based on an evolutionary neural network for autonomous flight in quadrotor systems. The controller’s objective is to govern the quadcopter in such a way that it reaches a specific position, bearing on attitude limitations during flight and upon reaching a target. Given the complex nature of quadcopters, an appropriate neural network architecture and a training algorithm were designed to guide a quadcopter toward a target. The designed controller was implemented as a single multi-layer perceptron. On the basis of the quadcopter’s current state, the developed neurocontroller produces the correct rotor speed values, optimized in terms of both attitude-limitation compliance and speed. The neural network training was completed using a custom evolutionary algorithm whose design put particular emphasis on the cost function’s definition. The developed neurocontroller was tested in simulation to drive a quadcopter to autonomously follow a complex path. The obtained simulated results show that the neurocontroller manages to effortlessly follow several types of paths with adequate precision while maintaining low travel times. Full article
(This article belongs to the Special Issue Artificial Intelligence in Drone Applications)
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22 pages, 6998 KiB  
Article
Decision-Making Strategies for Close-Range Air Combat Based on Reinforcement Learning with Variable-Scale Actions
by Lixin Wang, Jin Wang, Hailiang Liu and Ting Yue
Aerospace 2023, 10(5), 401; https://doi.org/10.3390/aerospace10050401 - 26 Apr 2023
Cited by 2 | Viewed by 1547
Abstract
The current research into decision-making strategies for air combat focuses on the performance of algorithms, while the selection of actions is often ignored, and the actions are often fixed in amplitude and limited in number in order to improve the convergence efficiency, making [...] Read more.
The current research into decision-making strategies for air combat focuses on the performance of algorithms, while the selection of actions is often ignored, and the actions are often fixed in amplitude and limited in number in order to improve the convergence efficiency, making the strategy unable to give full play to the maneuverability of the aircraft. In this paper, a decision-making strategy for close-range air combat based on reinforcement learning with variable-scale actions is proposed; the actions are the variable-scale virtual pursuit angles and speeds. Firstly, a trajectory prediction method consisting of a real-time prediction, correction, and judgment of errors is proposed. The back propagation (BP) neural network and the long and short term memory (LSTM) neural network are used as base prediction network and correction prediction network, respectively. Secondly, the past, current, and future positions of the target aircraft are used as virtual pursuit points, and they are converted into virtual pursuit angles as the track angle commands using angle guidance law. Then, the proximity policy optimization (PPO) algorithm is applied to train the agent. The simulation results show that the attacking aircraft that uses the strategy proposed in this paper has a higher win rate during air combat and the attacking aircraft’s maneuverability is fully utilized. Full article
(This article belongs to the Special Issue Artificial Intelligence in Drone Applications)
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18 pages, 3023 KiB  
Article
Autonomous Maneuver Decision of Air Combat Based on Simulated Operation Command and FRV-DDPG Algorithm
by Yongfeng Li, Yongxi Lyu, Jingping Shi and Weihua Li
Aerospace 2022, 9(11), 658; https://doi.org/10.3390/aerospace9110658 - 27 Oct 2022
Cited by 6 | Viewed by 2565
Abstract
With the improvement of UAV performance and intelligence in recent years, it is particularly important for unmanned aerial vehicles (UAVs) to improve the ability of autonomous air combat. Aiming to solve the problem of how to improve the autonomous air combat maneuver decision [...] Read more.
With the improvement of UAV performance and intelligence in recent years, it is particularly important for unmanned aerial vehicles (UAVs) to improve the ability of autonomous air combat. Aiming to solve the problem of how to improve the autonomous air combat maneuver decision ability of UAVs so that it can be close to manual manipulation, this paper proposes an autonomous air combat maneuvering decision method based on the combination of simulated operation command and the final reward value deep deterministic policy gradient (FRV-DDPG) algorithm. Firstly, the six-degree-of-freedom (6-DOF) model is established based on the air combat process, UAV motion, and missile motion. Secondly, a prediction method based on the Particle swarm optimization radial basis function (PSO-RBF) is designed to simulate the operation command of the enemy aircraft, which makes the training process more realistic, and then an improved DDPG strategy is proposed, which returns the final reward value to the previous reward value in a certain proportion of time for offline training, which can improve the convergence speed of the algorithm. Finally, the effectiveness of the algorithm is verified by building a simulation environment. The simulation results show that the algorithm can improve the autonomous air combat maneuver decision-making ability of UAVs. Full article
(This article belongs to the Special Issue Artificial Intelligence in Drone Applications)
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19 pages, 5286 KiB  
Article
A Multi-UCAV Cooperative Decision-Making Method Based on an MAPPO Algorithm for Beyond-Visual-Range Air Combat
by Xiaoxiong Liu, Yi Yin, Yuzhan Su and Ruichen Ming
Aerospace 2022, 9(10), 563; https://doi.org/10.3390/aerospace9100563 - 28 Sep 2022
Cited by 8 | Viewed by 2793
Abstract
To solve the problems of autonomous decision making and the cooperative operation of multiple unmanned combat aerial vehicles (UCAVs) in beyond-visual-range air combat, this paper proposes an air combat decision-making method that is based on a multi-agent proximal policy optimization (MAPPO) algorithm. Firstly, [...] Read more.
To solve the problems of autonomous decision making and the cooperative operation of multiple unmanned combat aerial vehicles (UCAVs) in beyond-visual-range air combat, this paper proposes an air combat decision-making method that is based on a multi-agent proximal policy optimization (MAPPO) algorithm. Firstly, the model of the unmanned combat aircraft is established on the simulation platform, and the corresponding maneuver library is designed. In order to simulate the real beyond-visual-range air combat, the missile attack area model is established, and the probability of damage occurring is given according to both the enemy and us. Secondly, to overcome the sparse return problem of traditional reinforcement learning, according to the angle, speed, altitude, distance of the unmanned combat aircraft, and the damage of the missile attack area, this paper designs a comprehensive reward function. Finally, the idea of centralized training and distributed implementation is adopted to improve the decision-making ability of the unmanned combat aircraft and improve the training efficiency of the algorithm. The simulation results show that this algorithm can carry out a multi-aircraft air combat confrontation drill, form new tactical decisions in the drill process, and provide new ideas for multi-UCAV air combat. Full article
(This article belongs to the Special Issue Artificial Intelligence in Drone Applications)
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