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Drones
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Unmanned Aerial Vehicles for Enhanced Emergency Response
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Unmanned Aerial Vehicles for Enhanced Emergency Response

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

Deadline for manuscript submissions: 20 September 2025 | Viewed by 4332

Special Issue Editors

Dr. Neng Ye

E-Mail Website
Guest Editor
School of Cyberspace Science and Technology/Beijing Institute of Technology, Beijing 100081, China
Interests: UAV Communications; next-generation radio access; information theory; non-orthogonal multiple access; coding theory and machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Wonjae Shin

E-Mail Website
Guest Editor
Department of Communication Engineering, School of Electrical Engineering, Korea University, Seoul 136-701, Republic of Korea
Interests: next-generation satellite communications; non-orthogonal multiple access; advanced interference management; machine learning-aided communication system design; deep learning-based indoor localization; wireless powered communications
Dr. Jianguo Li

E-Mail Website
Guest Editor
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: millimeter wave communication; high-precision radar; high data rate modulation; demodulation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiangming Li

E-Mail Website
Guest Editor
School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Interests: wireless and mobile communications; information theory and coding; information learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The emergency communication system is a crucial infrastructure that ensures connectivity in critical scenarios and core areas during emergencies and harsh environments. Unmanned-aerial-vehicle-based (UAV-based) emergency communication systems can overcome terrain limitations and offer flexible communication capability based on UAVs' mobility and multi-dimensional functions, including communication, sensing and computing. These capabilities make UAV-based systems a crucial part of the emergency response, along with the satellite communication system that offers ubiquitous coverage and terrestrial networks that provide the Internet connection.  However, UAV-based emergency communication systems still face challenges in many areas, including real-time situation awareness, autonomous navigation,  multi-UAV coordination, efficient on-the-fly data processing strategy under limited energy and processing capability, as well as the coordination of UAV with satellite and terrestrial networks, and secure information exchange of sensitive contexts.

The goal of this Special Issue is to gather original research articles and review papers that provide comprehensive insights into the application of UAVs in emergency response.

This Special Issue invites manuscripts covering the following themes:

  • Real-time sensor data gathering for situation awareness;
  • Advanced autonomous navigation algorithms for UAVs under dynamic environments;
  • Efficient communication systems for coordination among UAVs;
  • Edge computing strategy for on-the-fly data processing for UAVs;
  • Coordination of UAVs with satellite and terrestrial networks;
  • Secure information exchange in sensitive contexts for UAV-aided communication.

We welcome contributions ranging from original research articles to comprehensive reviews that explore the multifaceted applications of drones in emergency contexts.

Dr. Neng Ye
Dr. Wonjae Shin
Dr. Jianguo Li
Prof. Dr. Xiangming Li
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

  • UAVs
  • emergency response
  • situational awareness
  • autonomous navigation
  • edge computing
  • multi-UAV cooperation
  • space–air–ground-integrated network
  • secure information exchange

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

23 pages, 12360 KiB  
Article
Distributed Decision Making for Electromagnetic Radiation Source Localization Using Multi-Agent Deep Reinforcement Learning
by Jiteng Chen, Zehui Zhang, Dan Fan, Chaoqun Hou, Yue Zhang, Teng Hou, Xiangni Zou and Jun Zhao
Drones 2025, 9(3), 216; https://doi.org/10.3390/drones9030216 - 18 Mar 2025
Viewed by 326
Abstract
The detection and localization of radiation sources in urban areas present significant challenges in electromagnetic spectrum operations, particularly with the proliferation of small UAVs. To address these challenges, we propose the Multi-UAV Reconnaissance Proximal Policy Optimization (MURPPO) algorithm based on a distributed reinforcement [...] Read more.
The detection and localization of radiation sources in urban areas present significant challenges in electromagnetic spectrum operations, particularly with the proliferation of small UAVs. To address these challenges, we propose the Multi-UAV Reconnaissance Proximal Policy Optimization (MURPPO) algorithm based on a distributed reinforcement learning framework, which utilizes an independent decision making mechanism and collaborative positioning method with multiple UAVs to achieve high-precision detection and localization of radiation sources. We adopt a dual-branch actor structure for independent decisions in UAV control, which reduces decision complexity and improves learning efficiency. The algorithm incorporates task-specific knowledge into the reward function design to guide UAVs in exploring abnormal radiation sources. Furthermore, we employ a geometry-based three-point localization algorithm that leverages multiple UAVs’ spatial distribution for precise abnormal radiation source positioning. Simulations in urban environments demonstrate the effectiveness of the MURPPO algorithm, with the proportion of successfully localized target radiation sources converging to 56.5% in the later stages of training, approaching a 38.5% improvement over a traditional multi-agent proximal policy optimization algorithm. The results indicate that MURPPO effectively addresses the challenges of the intelligent sensing and localization of UAVs in complex urban electromagnetic spectrum operations. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)
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26 pages, 4783 KiB  
Article
A Hybrid Decision-Making Framework for UAV-Assisted MEC Systems: Integrating a Dynamic Adaptive Genetic Optimization Algorithm and Soft Actor–Critic Algorithm with Hierarchical Action Decomposition and Uncertainty-Quantified Critic Ensemble
by Yu Yang, Yanjun Shi, Xing Cui, Jiajian Li and Xijun Zhao
Drones 2025, 9(3), 206; https://doi.org/10.3390/drones9030206 - 13 Mar 2025
Viewed by 572
Abstract
With the continuous progress of UAV technology and the rapid development of mobile edge computing (MEC), the UAV-assisted MEC system has shown great application potential in special fields such as disaster rescue and emergency response. However, traditional deep reinforcement learning (DRL) decision-making methods [...] Read more.
With the continuous progress of UAV technology and the rapid development of mobile edge computing (MEC), the UAV-assisted MEC system has shown great application potential in special fields such as disaster rescue and emergency response. However, traditional deep reinforcement learning (DRL) decision-making methods suffer from limitations such as difficulty in balancing multiple objectives and training convergence when making mixed action space decisions for UAV path planning and task offloading. This article innovatively proposes a hybrid decision framework based on the improved Dynamic Adaptive Genetic Optimization Algorithm (DAGOA) and soft actor–critic with hierarchical action decomposition, an uncertainty-quantified critic ensemble, and adaptive entropy temperature, where DAGOA performs an effective search and optimization in discrete action space, while SAC can perform fine control and adjustment in continuous action space. By combining the above algorithms, the joint optimization of drone path planning and task offloading can be achieved, improving the overall performance of the system. The experimental results show that the framework offers significant advantages in improving system performance, reducing energy consumption, and enhancing task completion efficiency. When the system adopts a hybrid decision framework, the reward score increases by a maximum of 153.53% compared to pure deep reinforcement learning algorithms for decision-making. Moreover, it can achieve an average improvement of 61.09% on the basis of various reinforcement learning algorithms such as proposed SAC, proximal policy optimization (PPO), deep deterministic policy gradient (DDPG), and twin delayed deep deterministic policy gradient (TD3). Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)
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25 pages, 7621 KiB  
Article
UAV-Based Pseudolite Navigation System Architecture Design and the Flight Path Optimization
by Ruocheng Guo, Hong Yuan, Yang Zhang, Xiao Chen and Guanbing Zhang
Drones 2025, 9(2), 134; https://doi.org/10.3390/drones9020134 - 12 Feb 2025
Viewed by 571
Abstract
In a scenario where GNSS signal is blocked due to interference or occlusion, it is of considerable value to establish a regional navigation system providing emergency services for ground users by using long-endurance and long-range fixed-wing Unmanned Aerial Vehicles (UAVs). The main work [...] Read more.
In a scenario where GNSS signal is blocked due to interference or occlusion, it is of considerable value to establish a regional navigation system providing emergency services for ground users by using long-endurance and long-range fixed-wing Unmanned Aerial Vehicles (UAVs). The main work of this paper consists of two parts. First, we designed a set of UAV-based pseudolite navigation system (UAV-PNS) architecture based on fixed-wing UAVs. Then, considering the flight cost of the UAV swarm, the optimization of the UAV swarm’s flight path aimed at improving regional navigation performance was studied. In this paper, the fitness functions for UAVs’ flight path optimization are proposed, taking into account the navigation and positioning performance, the aircraft utilization rate of UAVs under flight constraints, and the response speed of the system to the emergency mission. Based on this, an acceptance–rejection mutated non-dominated sorting genetic algorithm III (ARMNSGA-III) is proposed for the UAVs’ flight path optimization. The research results show that the flight path strongly guarantees navigation service performance with constraints on the operating cost. The ARMNSGA-III proposed in this paper can provide a 44.01% algorithm timeliness improvement compared to the NSGA-III in the flight path optimization, supporting rapid establishment and continuous service of the UAV-PNS in emergency scenarios. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)
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35 pages, 1740 KiB  
Article
Distributed Cooperative Path Planning for Multi-UAV in Information-Rich and Dynamic Environments
by Pengfei Duan and Dawei Chen
Drones 2025, 9(1), 38; https://doi.org/10.3390/drones9010038 - 7 Jan 2025
Viewed by 998
Abstract
Accurate path planning is essential for effective regional avoidance in multiple unmanned aerial vehicle (multi-UAV) systems. Existing static path-planning techniques often fail to integrate multiple information sources, resulting in diminished performance in information-rich and dynamic environments. This paper proposes a distributed collaborative path-planning [...] Read more.
Accurate path planning is essential for effective regional avoidance in multiple unmanned aerial vehicle (multi-UAV) systems. Existing static path-planning techniques often fail to integrate multiple information sources, resulting in diminished performance in information-rich and dynamic environments. This paper proposes a distributed collaborative path-planning algorithm for dynamically changing targets in complex environments with multisource information. More specifically, a multi-UAV collaboration and path-planning method based on information-fusion technology is first presented to fuse the multisource data received by the UAVs from different platforms, such as space-based, air-based, and land-based. Subsequently, we introduce an algorithm to mark and divide the environment and hazardous areas, therefore enhancing overall situational awareness and eliminating visual blind spots in emergency communications scenarios. Furthermore, we develop an efficient, intelligent path-planning algorithm founded on objective functions and optimization methods at different stages, enabling UAVs to navigate safely while minimizing energy expenditure. Finally, the proposed strategy is validated through a simulation platform, demonstrating that the intelligent path-planning algorithm introduced in this study exhibits robust trajectory optimization capabilities in complex environments enriched with diverse information and potential threats. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)
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25 pages, 5516 KiB  
Article
Multi-UAV Path Planning for Air-Ground Relay Communication Based on Mix-Greedy MAPPO Algorithm
by Yiquan Wang, Yan Cui, Yu Yang, Zhaodong Li and Xing Cui
Drones 2024, 8(12), 706; https://doi.org/10.3390/drones8120706 - 26 Nov 2024
Viewed by 1064
Abstract
With the continuous development of modern UAV technology and communication technology, UAV-to-ground communication relay has become a research hotspot. In this paper, a Multi-Agent Reinforcement Learning (MARL) method based on the ε-greedy strategy and multi-agent proximal policy optimization (MAPPO) algorithm is proposed to [...] Read more.
With the continuous development of modern UAV technology and communication technology, UAV-to-ground communication relay has become a research hotspot. In this paper, a Multi-Agent Reinforcement Learning (MARL) method based on the ε-greedy strategy and multi-agent proximal policy optimization (MAPPO) algorithm is proposed to address the local optimization problem, improving the communication efficiency and task execution capability of UAV cluster control. This paper explores the path planning problem in multi-UAV-to-ground relay communication, with a special focus on the application of the proposed Mix-Greedy MAPPO algorithm. The state space, action space, communication model, training environment, and reward function are designed by comprehensively considering the actual tasks and entity characteristics such as safe distance, no-fly zones, survival in a threatened environment, and energy consumption. The results show that the Mix-Greedy MAPPO algorithm significantly improves communication probability, reduces energy consumption, avoids no-fly zones, and facilitates exploration compared to other algorithms in the multi-UAV ground communication relay path planning task. After training with the same number of steps, the Mix-Greedy MAPPO algorithm has an average reward score that is 45.9% higher than the MAPPO algorithm and several times higher than the multi-agent soft actor-critic (MASAC) and multi-agent deep deterministic policy gradient (MADDPG) algorithms. The experimental results verify the superiority and adaptability of the algorithm in complex environments. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles for Enhanced Emergency Response)
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