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Drones
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Cooperation of Drones and Other Manned/Unmanned Systems
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Cooperation of Drones and Other Manned/Unmanned Systems

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 21401

Special Issue Editors

Prof. Dr. Yu-Jun Zheng

E-Mail Website
Guest Editor
School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China
Interests: artificial intelligence; operations research; public health; UAV search and rescue
Special Issues, Collections and Topics in MDPI journals
Dr. Mumtaz Karatas

E-Mail Website
Guest Editor
Department of Industrial Engineering, Turkish Naval Academy, National Defense University, Istanbul 34940, Turkey
Interests: operations research; industrial engineering; decision sciences; optimization; location science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Drones, also known as unmanned aerial vehicles (UAVs) are becoming increasingly involved in various civilian and military applications. More specifically, there are many situations where the teaming of drones with other manned and unmanned systems (e.g., manned aircrafts, ground vehicles, surface vehicles) can promise significant economical, logistical, tactical, and other advantages. Examples include surveillance and reconnaissance missions, express and last-mile delivery tasks, search-and-rescue missions, scientific research, and combat operations, to name just a few. Nevertheless, the cooperation of drones and other manned/unmanned systems pose additional challenges, including task allocation and scheduling, route planning, communication and information sharing between different systems, interaction with environments, etc. A variety of technologies and solution approaches, such as bio-inspired computation, evolutionary computation, deep learning, and reinforcement learning, were employed to address these challenges.

This Special Issue aims to initiate a dialog on all aspects of hybrid drone and other manned/unmanned systems. In particular, we welcome studies bridging the gaps between research and practice, as well as studies across multiple disciplines.

Potential topics of interest include but are not limited to:

  • The cooperative use of drones and ground vehicles in logistics;
  • The cooperative use of drones and surface vehicles for maritime tasks;
  • The cooperative use of drones and manned aircrafts for military tasks;
  • The cooperative use of drones and other robots in difficult environments;
  • The cooperative and distributed use of small-scale drones with other systems;
  • Human–drone cooperation for reciprocal tasks;
  • Swarm intelligence algorithms for unmanned systems control;
  • Theories and models for hybrid manned–unmanned systems.

Prof. Dr. Yu-Jun Zheng
Dr. Mumtaz Karatas
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

  • drones
  • hybrid manned–unmanned systems
  • cooperation
  • optimization

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Published Papers (6 papers)

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Research

34 pages, 14879 KiB  
Article
Optimal Configuration of Heterogeneous Swarm for Cooperative Detection with Minimum DOP Based on Nested Cones
by Ruihang Yu, Yilin Liu, Yangtao Meng, Yan Guo, Zhiming Xiong and Pengfei Jiang
Drones 2024, 8(1), 11; https://doi.org/10.3390/drones8010011 - 2 Jan 2024
Cited by 1 | Viewed by 2007
Abstract
When unmanned platforms perform precise target detection, the configuration of detection nodes will significantly impact accuracy. Aiming to obtain the minimum dilution of precision (DOP), this paper innovatively proposes an optimal detection configuration design method focused on the heterogeneous unmanned cooperative swarm based [...] Read more.
When unmanned platforms perform precise target detection, the configuration of detection nodes will significantly impact accuracy. Aiming to obtain the minimum dilution of precision (DOP), this paper innovatively proposes an optimal detection configuration design method focused on the heterogeneous unmanned cooperative swarm based on the nested cone model. The proposed method first divides the swarm into different groups according to the performances of platforms and then uses a conical nested configuration to arrange the placement of each node independently. The paper considers the problem of the inaccurate prior position of the target and replaces the single-point DOP with the average DOP on the prior region of the target as the optimization objective. Considering the unavoidable positioning errors in engineering practice, this paper provides the optimal configuration of the detection group (DG) and anchor group (AG) in the swarm to reduce the impact caused by positioning errors of detection nodes. We set a certain swarm consisting of 3 types of platforms to design the configuration by simulation experiments and find the optimal parameters for nested cones to realize accurate detection. Full article
(This article belongs to the Special Issue Cooperation of Drones and Other Manned/Unmanned Systems)
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15 pages, 3214 KiB  
Article
Vision-Based Deep Reinforcement Learning of UAV-UGV Collaborative Landing Policy Using Automatic Curriculum
by Chang Wang, Jiaqing Wang, Changyun Wei, Yi Zhu, Dong Yin and Jie Li
Drones 2023, 7(11), 676; https://doi.org/10.3390/drones7110676 - 13 Nov 2023
Cited by 4 | Viewed by 3705
Abstract
Collaborative autonomous landing of a quadrotor Unmanned Aerial Vehicle (UAV) on a moving Unmanned Ground Vehicle (UGV) presents challenges due to the need for accurate real-time tracking of the UGV and the adjustment for the landing policy. To address this challenge, we propose [...] Read more.
Collaborative autonomous landing of a quadrotor Unmanned Aerial Vehicle (UAV) on a moving Unmanned Ground Vehicle (UGV) presents challenges due to the need for accurate real-time tracking of the UGV and the adjustment for the landing policy. To address this challenge, we propose a progressive learning framework for generating an optimal landing policy based on vision without the need of communication between the UAV and the UGV. First, we propose the Landing Vision System (LVS) to offer rapid localization and pose estimation of the UGV. Then, we design an Automatic Curriculum Learning (ACL) approach to learn the landing tasks under different conditions of UGV motions and wind interference. Specifically, we introduce a neural network-based difficulty discriminator to schedule the landing tasks according to their levels of difficulty. Our method achieves a higher landing success rate and accuracy compared with the state-of-the-art TD3 reinforcement learning algorithm. Full article
(This article belongs to the Special Issue Cooperation of Drones and Other Manned/Unmanned Systems)
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20 pages, 1312 KiB  
Article
Cooperative Truck–Drone Delivery Path Optimization under Urban Traffic Restriction
by Ying-Ying Weng, Rong-Yu Wu and Yu-Jun Zheng
Drones 2023, 7(1), 59; https://doi.org/10.3390/drones7010059 - 14 Jan 2023
Cited by 11 | Viewed by 3935
Abstract
In the traditional express delivery sector, trucks are the most available and efficient transportation mode in urban areas. However, due to the pressures of traffic congestion and air pollution problems, many cities have implemented strict measures to restrict trucks’ access to many zones [...] Read more.
In the traditional express delivery sector, trucks are the most available and efficient transportation mode in urban areas. However, due to the pressures of traffic congestion and air pollution problems, many cities have implemented strict measures to restrict trucks’ access to many zones during specified time periods, which has caused significant effects on the business of the industry. Due to their advantages, which include high speed, flexibility, and environmental friendliness, drones have great potential for being combined with trucks for efficient delivery in restricted traffic zones. In this paper, we propose a cooperative truck and drone delivery path optimization problem, in which a truck carrying cargo travels along the outer boundary of the restricted traffic zone to send and receive a drone, and the drone is responsible for delivering the cargo to customers. The objective of the problem is to minimize the completion time of all delivery tasks. To efficiently solve this problem, we propose a hybrid metaheuristic optimization algorithm to cooperatively optimize the outer path of the truck and the inner path of the drone. We conduct experiments on a set of test instances; the results demonstrate that the proposed algorithm exhibits a competitive performance compared to other selected popular optimization algorithms. Full article
(This article belongs to the Special Issue Cooperation of Drones and Other Manned/Unmanned Systems)
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21 pages, 1468 KiB  
Article
Wireless Communications for Data Security: Efficiency Assessment of Cybersecurity Industry—A Promising Application for UAVs
by Chia-Nan Wang, Fu-Chiang Yang, Nhut T. M. Vo and Van Thanh Tien Nguyen
Drones 2022, 6(11), 363; https://doi.org/10.3390/drones6110363 - 19 Nov 2022
Cited by 52 | Viewed by 4604
Abstract
The design of cooperative applications combining several unmanned aerial and aquatic vehicles is now possible thanks to the considerable advancements in wireless communication technology and the low production costs for small, unmanned vehicles. For example, the information delivered over the air instead of [...] Read more.
The design of cooperative applications combining several unmanned aerial and aquatic vehicles is now possible thanks to the considerable advancements in wireless communication technology and the low production costs for small, unmanned vehicles. For example, the information delivered over the air instead of inside an optical fiber causes it to be far simpler for an eavesdropper to intercept and improperly change the information. This article thoroughly analyzes the cybersecurity industry’s efficiency in addressing the rapidly expanding requirement to incorporate compelling security features into wireless communication systems. In this research, we used a combination of DEA window analysis with the Malmquist index approach to assess the efficiency of the cybersecurity industry. We used input and output factors utilizing financial data from 2017–2020 sources from a US market. It was found that U1—Synopsys and U9—Fortinet exhibited the best performances when relating Malmquist and DEA window analysis. By evaluating ten big companies in the cybersecurity industry, we indicate that U2—Palo Alto Networks and U6—BlackBerry Ltd. companies needed significant improvements and that four other companies were generally more efficient. The findings of this study provide decision-makers a clear image and it will be the first study to evaluate and predict the performance of cyber security organizations, providing a valuable reference for future research. Full article
(This article belongs to the Special Issue Cooperation of Drones and Other Manned/Unmanned Systems)
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17 pages, 5756 KiB  
Article
Deep Reinforcement Learning with Corrective Feedback for Autonomous UAV Landing on a Mobile Platform
by Lizhen Wu, Chang Wang, Pengpeng Zhang and Changyun Wei
Drones 2022, 6(9), 238; https://doi.org/10.3390/drones6090238 - 4 Sep 2022
Cited by 13 | Viewed by 2974
Abstract
Autonomous Unmanned Aerial Vehicle (UAV) landing remains a challenge in uncertain environments, e.g., landing on a mobile ground platform such as an Unmanned Ground Vehicle (UGV) without knowing its motion dynamics. A traditional PID (Proportional, Integral, Derivative) controller is a choice for the [...] Read more.
Autonomous Unmanned Aerial Vehicle (UAV) landing remains a challenge in uncertain environments, e.g., landing on a mobile ground platform such as an Unmanned Ground Vehicle (UGV) without knowing its motion dynamics. A traditional PID (Proportional, Integral, Derivative) controller is a choice for the UAV landing task, but it suffers the problem of manual parameter tuning, which becomes intractable if the initial landing condition changes or the mobile platform keeps moving. In this paper, we design a novel learning-based controller that integrates a standard PID module with a deep reinforcement learning module, which can automatically optimize the PID parameters for velocity control. In addition, corrective feedback based on heuristics of parameter tuning can speed up the learning process compared with traditional DRL algorithms that are typically time-consuming. In addition, the learned policy makes the UAV landing smooth and fast by allowing the UAV to adjust its speed adaptively according to the dynamics of the environment. We demonstrate the effectiveness of the proposed algorithm in a variety of quadrotor UAV landing tasks with both static and dynamic environmental settings. Full article
(This article belongs to the Special Issue Cooperation of Drones and Other Manned/Unmanned Systems)
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19 pages, 2854 KiB  
Article
A Design Approach for Simultaneous Cooperative Interception Based on Area Coverage Optimization
by Long Wang, Kai Liu, Yu Yao and Fenghua He
Drones 2022, 6(7), 156; https://doi.org/10.3390/drones6070156 - 24 Jun 2022
Cited by 6 | Viewed by 2087
Abstract
In this paper, a design approach for simultaneous cooperative interception is presented for a scenario where the successful handover cannot be guaranteed by a single interceptor due to the target maneuver and movement information errors at the handover moment. Firstly, the concepts of [...] Read more.
In this paper, a design approach for simultaneous cooperative interception is presented for a scenario where the successful handover cannot be guaranteed by a single interceptor due to the target maneuver and movement information errors at the handover moment. Firstly, the concepts of the reachable interception area and predicted interception area are introduced, a performance index function is constructed, and the probability of a successful handover is described by considering the coverage of the predicted interception area. Taking the probability of successful handover as a constraint, the simultaneous cooperative interception design problem is formulated based on area coverage. Then, an area coverage optimization algorithm is presented to design the spatial distributions of the interceptors. In order to enhance the handover probability, a simultaneous cooperative interception design approach is proposed to obtain the number of interceptors and the corresponding spatial distributions. Finally, simulation experiments are carried out to validate the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Cooperation of Drones and Other Manned/Unmanned Systems)
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