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Applied Sciences
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Intelligent Control of Unmanned Aerial Vehicles
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Intelligent Control of Unmanned Aerial Vehicles

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 2599

Special Issue Editor

Dr. Wei Yue

E-Mail Website
Guest Editor
School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116000, China
Interests: connected and automated vehicle control; multi-UAV cooperative control; intelligent swarm cooperative search

Special Issue Information

Dear Colleagues,

Background:

Unmanned aerial vehicles (UAVs) have attracted great attention in recent years due to their versatile applications in many fields, such as aerial photography, search and rescue, environmental monitoring, and military missions. The development of UAVs has advanced rapidly, with more sophisticated sensors, better communication capabilities, and more powerful computing resources. However, one critical challenge for UAVs is the need for intelligent control to operate in dynamic and unpredictable environments.

Intelligent control, which aims to enhance the autonomy and decision-making capabilities of UAVs, is a critical research area that is attracting growing interest from both academia and industry. Intelligent control can enable UAVs to operate autonomously, adaptively, and robustly, and complete tasks with high accuracy and safety. Intelligent control of UAVs involves the integration of various techniques from computer vision, control theory, machine learning, and artificial intelligence.

In recent years, research in the intelligent control of UAVs has made significant progress in both theoretical and practical aspects. However, there are still many challenges to be addressed, such as the scalability of intelligent control algorithms, the energy efficiency of UAVs, and the safety and security of UAV operations. This Special Issue aims to present the latest research progress and innovative solutions in the field of intelligent control of UAVs, and promote collaborations and discussions among researchers, engineers, and practitioners.

Content:

UAVs are widely used in a variety of fields, such as transportation, military, agriculture, and environment monitoring. However, the increasing complexity of UAV applications demands more intelligent control capabilities, which requires advanced algorithms and perception technologies. This Special Issue will focus on the intelligent control of UAVs, including but not limited to the following topics:

  1. Intelligent navigation and control for multi-UAV systems: research on coordination and control algorithms for multiple UAVs in swarm systems, which can achieve various tasks more efficiently and flexibly.
  2. Machine learning-based intelligent control: the applications of machine learning techniques in UAV intelligent control, including reinforcement learning, deep learning, and other learning methods to improve the perception and decision-making capabilities of UAVs.
  3. Intelligent energy management for UAVs: the study of energy optimization and management for UAVs, which can extend flight time and reduce energy consumption, and increase the autonomy and efficiency of UAVs.
  4. Intelligent perception and decision-making for UAVs: research on perception and decision-making models for UAVs, including intelligent perception fusion, decision-making reasoning, and planning, which can improve the performance and robustness of UAVs in complex environments.
  5. Applications of intelligent control of UAVs in fields such as aerial photography, search and rescue, environmental monitoring, and agriculture.

The papers selected for this Special Issue will be evaluated by a rigorous peer-review process, and accepted papers will be published in a reputable journal. We invite researchers and experts from academia and industry to contribute their innovative and high-quality research on the intelligent control of UAVs.

Dr. Wei Yue
Guest Editor

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.

Published Papers (3 papers)

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20 pages, 4048 KiB  
Article
Bio-Inspired Fission–Fusion Control and Planning of Unmanned Aerial Vehicles Swarm Systems via Reinforcement Learning
by Xiaorong Zhang, Yufeng Wang, Wenrui Ding, Qing Wang, Zhilan Zhang and Jun Jia
Appl. Sci. 2024, 14(3), 1192; https://doi.org/10.3390/app14031192 - 31 Jan 2024
Viewed by 451
Abstract
Swarm control of unmanned aerial vehicles (UAV) has emerged as a challenging research area, primarily attributed to the presence of conflicting behaviors among individual UAVs and the influence of external movement disturbances of UAV swarms. However, limited attention has been drawn to addressing [...] Read more.
Swarm control of unmanned aerial vehicles (UAV) has emerged as a challenging research area, primarily attributed to the presence of conflicting behaviors among individual UAVs and the influence of external movement disturbances of UAV swarms. However, limited attention has been drawn to addressing the fission–fusion motion of UAV swarms for unknown dynamic obstacles, as opposed to static ones. A Bio-inspired Fission–Fusion control and planning via Reinforcement Learning (BiFRL) algorithm for the UAV swarm system is presented, which tackles the problem of fission–fusion behavior in the presence of dynamic obstacles with homing capabilities. Firstly, we found the kinematics models for the UAV and swarm controller, and then we proposed a probabilistic starling-inspired topological interaction that achieves reduced overhead communication and faster local convergence. Next, we develop a self-organized fission–fusion control framework and a fission decision algorithm. When dealing with various situations, the swarm can autonomously re-configure itself by fissioning an optimal number of agents to fulfill the corresponding tasks. Finally, we design a sub-swarm confrontation algorithm for path planning optimized by reinforcement learning, where the sub-swarm can engage in encounters with dynamic obstacles while minimizing energy expenditure. Simulation experiments demonstrate the capability of the UAV swarm system to accomplish self-organized fission–fusion control and planning under different interference scenarios. Moreover, the proposed BiFRL algorithm successfully handles adversarial motion with dynamic obstacles and effectively safeguards the parent swarm. Full article
(This article belongs to the Special Issue Intelligent Control of Unmanned Aerial Vehicles)
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17 pages, 6980 KiB  
Article
A Maturity Detection Method for Hemerocallis Citrina Baroni Based on Lightweight and Attention Mechanism
by Bin Sheng, Ligang Wu and Nan Zhang
Appl. Sci. 2023, 13(21), 12043; https://doi.org/10.3390/app132112043 - 04 Nov 2023
Viewed by 839
Abstract
Hemerocallis citrina Baroni with different maturity levels has different uses for food and medicine and has different economic benefits and sales value. However, the growth speed of Hemerocallis citrina Baroni is fast, the harvesting cycle is short, and the maturity identification is completely [...] Read more.
Hemerocallis citrina Baroni with different maturity levels has different uses for food and medicine and has different economic benefits and sales value. However, the growth speed of Hemerocallis citrina Baroni is fast, the harvesting cycle is short, and the maturity identification is completely dependent on experience, so the harvesting efficiency is low, the dependence on manual labor is large, and the identification standard is not uniform. In this paper, we propose a GCB YOLOv7 Hemerocallis citrina Baroni maturity detection method based on a lightweight neural network and attention mechanism. First, lightweight Ghost convolution is introduced to reduce the difficulty of feature extraction and decrease the number of computations and parameters of the model. Second, between the feature extraction backbone network and the feature fusion network, the CBAM mechanism is added to perform the feature extraction independently in the channel and spatial dimensions, which improves the tendency of the feature extraction and enhances the expressive ability of the model. Last, in the feature fusion network, Bi FPN is used instead of the concatenate feature fusion method, which increases the information fusion channels while decreasing the number of edge nodes and realizing cross-channel information fusion. The experimental results show that the improved GCB YOLOv7 algorithm reduces the number of parameters and floating-point operations by about 2.03 million and 7.3 G, respectively. The training time is reduced by about 0.122 h, and the model volume is compressed from 74.8 M to 70.8 M. In addition, the average precision is improved from 91.3% to 92.2%, mAP@0.5 and mAP@0.5:0.95 are improved by about 1.38% and 0.20%, respectively, and the detection efficiency reaches 10 ms/frame, which meets the real-time performance requirements. It can be seen that the improved GCB YOLOv7 algorithm is not only lightweight but also effectively improves detection precision. Full article
(This article belongs to the Special Issue Intelligent Control of Unmanned Aerial Vehicles)
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23 pages, 6206 KiB  
Article
Research on Scheme Design and Decision of Multiple Unmanned Aerial Vehicle Cooperation Anti-Submarine Based on Knowledge-Driven Soft Actor-Critic
by Xiaoyong Zhang, Wei Yue and Wenbin Tang
Appl. Sci. 2023, 13(20), 11527; https://doi.org/10.3390/app132011527 - 20 Oct 2023
Viewed by 789
Abstract
To enhance the anti-submarine and search capabilities of multiple Unmanned Aerial Vehicle (UAV) groups in complex marine environments, this paper proposes a flexible action-evaluation algorithm known as Knowledge-Driven Soft Actor-Critic (KD-SAC), which can effectively interact with real-time environmental information. KD-SAC is a reinforcement [...] Read more.
To enhance the anti-submarine and search capabilities of multiple Unmanned Aerial Vehicle (UAV) groups in complex marine environments, this paper proposes a flexible action-evaluation algorithm known as Knowledge-Driven Soft Actor-Critic (KD-SAC), which can effectively interact with real-time environmental information. KD-SAC is a reinforcement learning algorithm that consists of two main components: UAV Group Search Knowledge Base (UGSKB) and path planning strategy. Firstly, based on the UGSKB, we establish a cooperation search framework that comprises three layers of information models: the data layer provides prior information and fundamental search rules to the system, the knowledge layer enriches search rules and database in continuous searching processes, and the decision layer utilizes above two layers of information models to enable autonomous decision-making by UAVs. Secondly, we propose a rule-based deductive inference return visit (RDIRV) strategy to enhance the knowledge base of search. The core concept of this strategy is to enable UAVs to learn from both successful and unsuccessful experiences, thereby enriching the search rules based on optimal decisions as exemplary cases. This approach can significantly enhance the learning performance of KD-SAC. The subsequent step involves designing an event-based UGSKB calling mechanism at the decision-making level, which calls a template based on the target and current motion. Finally, it uses a punishment function, and is then employed to achieve optimal decision-making for UAV actions and states. The feasibility and superiority of our proposed algorithm are demonstrated through experimental comparisons with alternative methods. The final results demonstrate that the proposed method achieves a success rate of 73.63% in multi-UAV flight path planning within complex environments, surpassing the other three algorithms by 17.27%, 29.88%, and 33.51%, respectively. In addition, the KD-SAC algorithm outperforms the other three algorithms in terms of synergy and average search reward. Full article
(This article belongs to the Special Issue Intelligent Control of Unmanned Aerial Vehicles)
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