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Drones
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Intelligent Autonomous Control and Swarm Cooperative Control of Unmanned Systems
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Intelligent Autonomous Control and Swarm Cooperative Control of Unmanned Systems

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drone Communications".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 9668

Special Issue Editors

Dr. Ziyang Zhen

E-Mail Website
Guest Editor
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Interests: guidance, navigation and control; formation/swarm cooperative control
Assoc. Prof. Dr. Jia Song

E-Mail Website
Guest Editor
School of Astronautics, Beihang University, Beijing 100191, China
Interests: autonomous fault diagnosis based on hybrid intelligence; disturbance rejection and fault-tolerant guidance control for unmanned aerial vehicle; cooperative control of multi-agent based on hybrid intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned systems, which include UAVs, UUVs, USVs, UGVs, and so on, have become a hot high-tech industry due to their variety, flexible use, and wide application. Unmanned systems are developing towards the direction of autonomy, intelligence, and clustering. Intelligent autonomous control and swarm cooperative control of unmanned systems is the emerging product of the deep integration of unmanned systems, artificial intelligence, autonomous control, and swarm cooperative control and is becoming the frontier hotspot in the current academic theory and application field, receiving great attention in many countries.

This Special Issue aims to provide a high-end academic exchange platform for domestic experts and scholars in the field of US autonomous control, swarm intelligence, and cooperative control and to share advanced theories, key technologies, and application achievements. 

Both research papers and overview papers are welcome. Topics of interest include (but are not limited to) the following:

  • Intelligent autonomous control of unmanned systems
  • Cooperative control of manned/unmanned systems
  • Multi-domain cooperative control of unmanned systems
  • Mission planning of unmanned systems
  • Cooperative control of UAV swarm
  • Guidance, navigation, and control of unmanned systems

Prof. Dr. Ziyang Zhen
Assoc. Prof. Dr. Jia Song
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 2600 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 
  • unmanned aerial vehicle 
  • UAV swarm 
  • cooperative control 
  • swarm intelligence 
  • autonomous control 
  • guidance, navigation and control

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Published Papers (5 papers)

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Research

16 pages, 4432 KiB  
Article
Research on Multi-UAV Cooperative Dynamic Path Planning Algorithm Based on Conflict Search
by Zhigang Wang, Huajun Gong, Mingtao Nie and Xiaoxiong Liu
Drones 2024, 8(6), 274; https://doi.org/10.3390/drones8060274 - 20 Jun 2024
Cited by 1 | Viewed by 1575
Abstract
Considering of the dynamic cooperative path planning problem of multiple UAVs in complex environments, this paper further considers the flight constraints, space coordination, and fast re-planning of UAVs after detecting sudden obstacles on the basis of conflict-based search algorithm (CBS). A sparse CBS-D* [...] Read more.
Considering of the dynamic cooperative path planning problem of multiple UAVs in complex environments, this paper further considers the flight constraints, space coordination, and fast re-planning of UAVs after detecting sudden obstacles on the basis of conflict-based search algorithm (CBS). A sparse CBS-D* algorithm is proposed as a cooperative dynamic path planning algorithm for UAVs in sudden threats. The algorithm adopts the two-layer planning idea. At the low layer, a sparse D* algorithm, which can realize the 3D dynamic path planning of UAVs, is proposed by combining the dynamic constraints of UAVs with the D* algorithm. At the high layer, heuristic information is introduced into the cost function to improve the search efficiency, and a dynamic response mechanism is designed to realize rapid re-planning in the face of sudden threats. The simulation results show that the proposed algorithm can deal with the UAV cooperative dynamic path planning problem in a complex environment more quickly and effectively. Full article
(This article belongs to the Special Issue Intelligent Autonomous Control and Swarm Cooperative Control of Unmanned Systems)
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22 pages, 6253 KiB  
Article
Probabilistic Chain-Enhanced Parallel Genetic Algorithm for UAV Reconnaissance Task Assignment
by Jiaze Tang, Dan Liu, Qisong Wang, Junbao Li and Jinwei Sun
Drones 2024, 8(6), 213; https://doi.org/10.3390/drones8060213 - 21 May 2024
Cited by 3 | Viewed by 1075
Abstract
With the increasing diversity and complexity of tasks assigned to unmanned aerial vehicles (UAVs), the demands on task assignment and sequencing technologies have grown significantly, particularly for large UAV tasks such as multi-target reconnaissance area surveillance. While the current exhaustive methods offer thorough [...] Read more.
With the increasing diversity and complexity of tasks assigned to unmanned aerial vehicles (UAVs), the demands on task assignment and sequencing technologies have grown significantly, particularly for large UAV tasks such as multi-target reconnaissance area surveillance. While the current exhaustive methods offer thorough solutions, they encounter substantial challenges in addressing large-scale task assignments due to their extensive computational demands. Conversely, while heuristic algorithms are capable of delivering satisfactory solutions with limited computational resources, they frequently struggle with converging on locally optimal solutions and are characterized by low iteration rates. In response to these limitations, this paper presents a novel approach: the probabilistic chain-enhanced parallel genetic algorithm (PC-EPGA). The PC-EPGA combines probabilistic chains with genetic algorithms to significantly enhance the quality of solutions. In our approach, each UAV flight is considered a Dubins vehicle, incorporating kinematic constraints. In addition, it integrates parallel genetic algorithms to improve hardware performance and processing speed. In our study, we represent task points as chromosome nodes and construct probabilistic connection chains between these nodes. This structure is specifically designed to influence the genetic algorithm’s crossover and mutation processes by taking into account both the quantity of tasks assigned to UAVs and the associated costs of inter-task flights. In addition, we propose a fitness-based adaptive crossover operator to circumvent local optima more effectively. To optimize the parameters of the PC-EPGA, Bayesian networks are utilized, which improves the efficiency of the whole parameter search process. The experimental results show that compared to the traditional heuristic algorithms, the probabilistic chain algorithm significantly improves the quality of solutions and computational efficiency. Full article
(This article belongs to the Special Issue Intelligent Autonomous Control and Swarm Cooperative Control of Unmanned Systems)
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16 pages, 2515 KiB  
Article
Co-Evolutionary Algorithm-Based Multi-Unmanned Aerial Vehicle Cooperative Path Planning
by Yan Wu, Mingtao Nie, Xiaolei Ma, Yicong Guo and Xiaoxiong Liu
Drones 2023, 7(10), 606; https://doi.org/10.3390/drones7100606 - 26 Sep 2023
Cited by 11 | Viewed by 2100
Abstract
Multi-UAV cooperative path planning is a key technology to carry out multi-UAV tasks, and its research has important practical significance. A multi-UAV cooperative path is a combination of single-UAV paths, so the idea of problem decomposition is effective to deal with multi-UAV cooperative [...] Read more.
Multi-UAV cooperative path planning is a key technology to carry out multi-UAV tasks, and its research has important practical significance. A multi-UAV cooperative path is a combination of single-UAV paths, so the idea of problem decomposition is effective to deal with multi-UAV cooperative path planning. With this analysis, a multi-UAV cooperative path planning algorithm based on co-evolution optimization was proposed in this paper. Firstly, by analyzing the meaning of multi-UAV cooperative flight, the optimization model of multi-UAV cooperative path planning was given. Secondly, we designed the cost function of multiple UAVs with the penalty function method to deal with multiple constraints and designed two information-sharing strategies to deal with the combination path search between multiple UAVs. The two information-sharing strategies were called the optimal individual selection strategy and the mixed selection strategy. The new cooperative path planning algorithm was presented by combining the above designation and co-evolution algorithm. Finally, the proposed algorithm is applied to a rendezvous task in complex environments and compared with two evolutionary algorithms. The experimental results show that the proposed algorithm can effectively cope with the multi-UAV cooperative path planning problem in complex environments. Full article
(This article belongs to the Special Issue Intelligent Autonomous Control and Swarm Cooperative Control of Unmanned Systems)
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23 pages, 11660 KiB  
Article
Formation Transformation Based on Improved Genetic Algorithm and Distributed Model Predictive Control
by Guanyu Chen, Congwei Zhao, Huajun Gong, Shuai Zhang and Xinhua Wang
Drones 2023, 7(8), 527; https://doi.org/10.3390/drones7080527 - 11 Aug 2023
Cited by 1 | Viewed by 1195
Abstract
In order to solve the problem of multiple aircraft formation transformation to a designated formation, a distributed formation transformation algorithm that decomposes the formation transformation problem into target-matching problems and trajectory-planning problems was studied. According to the actual formation transformation requirements, the target [...] Read more.
In order to solve the problem of multiple aircraft formation transformation to a designated formation, a distributed formation transformation algorithm that decomposes the formation transformation problem into target-matching problems and trajectory-planning problems was studied. According to the actual formation transformation requirements, the target allocation index was proposed, and the improved genetic algorithm which is 23% better than other algorithms was used to achieve target matching. The adaptive cross-mutation probability was designed, and the population was propagated without duplicates by the hash algorithm. The multi-objective algorithm of distributed model predictive control was used to design smooth and conflict-free trajectories for the UAVs in formation transformation, and the trajectory-planning problem was transformed into a quadratic programming problem under inequality constraints. Finally, point-to-point collision-free offline trajectory planning was realized by simulation. Full article
(This article belongs to the Special Issue Intelligent Autonomous Control and Swarm Cooperative Control of Unmanned Systems)
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20 pages, 8360 KiB  
Article
Multi-UAV Cooperative Obstacle Avoidance of 3D Vector Field Histogram Plus and Dynamic Window Approach
by Xinhua Wang, Mingyan Cheng, Shuai Zhang and Huajun Gong
Drones 2023, 7(8), 504; https://doi.org/10.3390/drones7080504 - 2 Aug 2023
Cited by 6 | Viewed by 2099
Abstract
In this paper, we propose a fusion algorithm that integrates the 3D vector field histogram plus (VFH+) algorithm and the improved dynamic window approach (DWA) algorithm. The aim is to address the challenge of cooperative obstacle avoidance faced by multi-UAV formation flying in [...] Read more.
In this paper, we propose a fusion algorithm that integrates the 3D vector field histogram plus (VFH+) algorithm and the improved dynamic window approach (DWA) algorithm. The aim is to address the challenge of cooperative obstacle avoidance faced by multi-UAV formation flying in unknown environments. First, according to the navigation evaluation function of the standard DWA algorithm, the target distance is introduced to correct the azimuth. Then, aiming at the problem that the fixed weight mechanism in standard DWA algorithm is unreasonable, we combine the A* algorithm and introduce variable weight factors related to azimuth to improve the target orientation ability in local path planning. A new rotation cost evaluation function is added to improve the obstacle avoidance ability of two-dimensional UAV. Then, 3D VFH+ algorithm is introduced and integrated with improved DWA algorithm to design a distributed cooperative formation obstacle avoidance control algorithm. Simulation validation suggests that compared with the traditional DWA algorithm, the improved collaborative obstacle avoidance control algorithm can greatly optimize the obstacle avoidance effect of UAVs’ formation flight. Full article
(This article belongs to the Special Issue Intelligent Autonomous Control and Swarm Cooperative Control of Unmanned Systems)
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