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Intelligent Unmanned System Technology and Application
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Intelligent Unmanned System Technology and Application

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (30 August 2023) | Viewed by 5317

Special Issue Editors

Prof. Dr. Hanqiao Huang

E-Mail Website
Guest Editor
Unmanned System Research Institute, Northwestern Polytechnical University, Xi’an 710072, China
Interests: cooperative gudance and control; nonlinear control; intelligent decision-making; machine learning in aerospace engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Maopeng Ran

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: robust control; intelligent control; formation control

Special Issue Information

Dear Colleagues,

Unmanned systems can complete specific missions without human participation, and play an important role in military and economy. It can not only implement disaster relief, specifically self-driving, but can also display skills in electronic jamming, air defense suppression, air confrontation, and other aspects. Improvements in the intelligence level play a crucial role in improving the efficiency of unmanned system. Some problems are important for the development of intelligent unmanned systems, such as modelling, sensing, and control.

Despite decades of development, intelligent unmanned system technology still faces challenges in theory and practice. In this context, this Special Issue aims to collect and report the latest developments in intelligent unmanned system technology and application. Reviews and original research articles are very welcome. We look forward to your active participation in this Special Issue.

Prof. Dr. Hanqiao Huang
Prof. Dr. Maopeng Ran
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. Applied Sciences 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 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.

Keywords

  • unmanned system
  • intelligent control
  • autonomous decision
  • path planning
  • autonomous detection
  • cooperative guidance

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Published Papers (5 papers)

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Research

16 pages, 2320 KiB  
Article
Meta-Learning-Based Incremental Nonlinear Dynamic Inversion Control for Quadrotors with Disturbances
by Xinyue Zhang and Maopeng Ran
Appl. Sci. 2023, 13(21), 11844; https://doi.org/10.3390/app132111844 - 30 Oct 2023
Cited by 1 | Viewed by 966
Abstract
This paper proposes an online meta-learning-based incremental nonlinear dynamic inversion (INDI) control method for quadrotors with disturbances. The quadrotor dynamic model is first transformed into linear form via an INDI control law. Since INDI largely depends on the accuracy of the control matrix, [...] Read more.
This paper proposes an online meta-learning-based incremental nonlinear dynamic inversion (INDI) control method for quadrotors with disturbances. The quadrotor dynamic model is first transformed into linear form via an INDI control law. Since INDI largely depends on the accuracy of the control matrix, a method composed of meta-learning and adaptive control is proposed to estimate it online. The effectiveness of the proposed control framework is validated through simulation on a quadrotor with 3D wind disturbances. Full article
(This article belongs to the Special Issue Intelligent Unmanned System Technology and Application)
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21 pages, 2866 KiB  
Article
A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks
by Qin Zhong, Jie Zhou and Yao Zhang
Appl. Sci. 2023, 13(21), 11700; https://doi.org/10.3390/app132111700 - 26 Oct 2023
Viewed by 822
Abstract
Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be realized to expand the detection range, and more complex tasks can be [...] Read more.
Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be realized to expand the detection range, and more complex tasks can be completed without human participation, so as to improve work efficiency and reduce the consumption of manpower and material resources. In AUSCNs, clustering is a key method to lower energy consumption. However, there is a challenge to select cluster heads in AUSCNs because of the limitation of transmission distances and the complexity of network topological structures. In addition, this problem has been confirmed as NP-hard. In this paper, a new niche adaptive elite evolutionary algorithm (NAEEA) is proposed to solve this problem. NAEEAs can search within various complicated stochastic situations at high speeds with characterized high precision and fast convergence. This algorithm integrates the merits of elite selection and adaptive adjusting to achieve high performance. In NAEEAs, a new adaptive operator is designed to speed up the convergence rate, while a novel elite operator is proposed to avoid local optima and raise the exploration ability. Furthermore, a new niche operator is also proposed to increase the population diversity. The simulation results show that, compared with an evolutionary algorithm (EA), a simulated annealing algorithm (SA) and a leapfrog algorithm (SFLA), clustering energy consumption based on an NAEEA is significantly reduced, and the network energy consumption of the AUSCN is up to 21.43%, 25.00% and 25.76% lower than the other three algorithms, respectively. Full article
(This article belongs to the Special Issue Intelligent Unmanned System Technology and Application)
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17 pages, 2367 KiB  
Article
A Novel Chaotic Elite Adaptive Genetic Algorithm for Task Allocation of Intelligent Unmanned Wireless Sensor Networks
by Hongmei Fei, Baitao Zhang, Yan Liu, Manli Yan, Yi Lu and Jie Zhou
Appl. Sci. 2023, 13(17), 9870; https://doi.org/10.3390/app13179870 - 31 Aug 2023
Cited by 1 | Viewed by 863
Abstract
In recent times, the progress of Intelligent Unmanned Wireless Sensor Networks (IUWSNs) has inspired scientists to develop inventive task allocation algorithms. These efficient techniques serve as robust stochastic optimization methods, aimed at maximizing revenue for the network’s objectives. However, with the increase in [...] Read more.
In recent times, the progress of Intelligent Unmanned Wireless Sensor Networks (IUWSNs) has inspired scientists to develop inventive task allocation algorithms. These efficient techniques serve as robust stochastic optimization methods, aimed at maximizing revenue for the network’s objectives. However, with the increase in sensor numbers, the computation time for addressing the challenge grows exponentially. To tackle the task allocation issue in IUWSNs, this paper introduces a novel approach: the Chaotic Elite Adaptive Genetic Algorithm (CEAGA). The optimization problem is formulated as an NP-complete integer programming challenge. Innovative elite and chaotic operators have been devised to expedite convergence and unveil the overall optimal solution. By merging the strengths of genetic algorithms with these new elite and chaotic operators, the CEAGA optimizes task allocation in IUWSNs. Through simulation experiments, we compare the CEAGA with other methods—Hybrid Genetic Algorithm (HGA), Multi-objective Binary Particle Swarm Optimization (MBPSO), and Improved Simulated Annealing (ISA)—in terms of task allocation performance. The results compellingly demonstrate that the CEAGA outperforms the other approaches in network revenue terms. Full article
(This article belongs to the Special Issue Intelligent Unmanned System Technology and Application)
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15 pages, 5925 KiB  
Article
Maneuver Decision-Making through Automatic Curriculum Reinforcement Learning without Handcrafted Reward Functions
by Yujie Wei, Hongpeng Zhang, Yuan Wang and Changqiang Huang
Appl. Sci. 2023, 13(16), 9421; https://doi.org/10.3390/app13169421 - 19 Aug 2023
Viewed by 870
Abstract
Maneuver decision-making is essential for autonomous air combat. However, previous methods usually make decisions to aim at the target instead of hitting the target and use discrete action spaces instead of continuous action spaces. While these simplifications make maneuver decision-making easier, they also [...] Read more.
Maneuver decision-making is essential for autonomous air combat. However, previous methods usually make decisions to aim at the target instead of hitting the target and use discrete action spaces instead of continuous action spaces. While these simplifications make maneuver decision-making easier, they also make maneuver decision-making more unrealistic. Meanwhile, previous studies usually rely on handcrafted reward functions, which are troublesome to design. Therefore, to solve these problems, we propose an automatic curriculum reinforcement learning method that enables agents to maneuver effectively in air combat from scratch. On the basis of curriculum reinforcement learning, maneuver decision-making is divided into a series of sub-tasks from easy to difficult. Thus, agents can gradually learn how to complete a series of sub-tasks, from easy to difficult without handcrafted reward functions. The ablation studies show that automatic curriculum learning is essential for reinforcement learning; namely, agents cannot make effective decisions without curriculum learning. Simulations show that, after training, agents are able to make effective decisions given different states, including tracking, attacking, and escaping, which are both rational and interpretable. Full article
(This article belongs to the Special Issue Intelligent Unmanned System Technology and Application)
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17 pages, 838 KiB  
Article
Multi-Agent Chronological Planning with Model-Agnostic Meta Reinforcement Learning
by Cong Hu, Kai Xu, Zhengqiu Zhu, Long Qin and Quanjun Yin
Appl. Sci. 2023, 13(16), 9174; https://doi.org/10.3390/app13169174 - 11 Aug 2023
Viewed by 1080
Abstract
In this study, we propose an innovative approach to address a chronological planning problem involving the multiple agents required to complete tasks under precedence constraints. We model this problem as a stochastic game and solve it with multi-agent reinforcement learning algorithms. However, these [...] Read more.
In this study, we propose an innovative approach to address a chronological planning problem involving the multiple agents required to complete tasks under precedence constraints. We model this problem as a stochastic game and solve it with multi-agent reinforcement learning algorithms. However, these algorithms necessitate relearning from scratch when confronted with changes in the chronological order of tasks, resulting in distinct stochastic games and consuming a substantial amount of time. To overcome this challenge, we present a novel framework that incorporates meta-learning into a multi-agent reinforcement learning algorithm. This approach enables the extraction of meta-parameters from past experiences, facilitating rapid adaptation to new tasks with altered chronological orders and circumventing the time-intensive nature of reinforcement learning. Then, the proposed framework is demonstrated through the implementation of a method named Reptile-MADDPG. The performance of the pre-trained model is evaluated using average rewards before and after fine-tuning. Our method, in two testing tasks, improves the average rewards from −44 to −37 through 10,000 steps of fine-tuning in two testing tasks, significantly surpassing the two baseline methods that only attained −51 and −44, respectively. The experimental results demonstrate the superior generalization capabilities of our method across various tasks, thus constituting a significant contribution towards the design of intelligent unmanned systems. Full article
(This article belongs to the Special Issue Intelligent Unmanned System Technology and Application)
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