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Drones
Special Issues
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: 20 June 2024 | Viewed by 4538

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

Published Papers (3 papers)

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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 1 | Viewed by 1192
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
Viewed by 817
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 - 02 Aug 2023
Viewed by 1278
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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