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Keywords = swarming flight system

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14 pages, 845 KiB  
Article
Cross-Path Planning of UAV Cluster Low-Altitude Flight Based on Inertial Navigation Combined with GPS Localization
by Xiancheng Yang, Ming Zhang, Peihui Yan, Qu Wang, Dongpeng Xie and Yuntian Brian Bai
Electronics 2025, 14(14), 2877; https://doi.org/10.3390/electronics14142877 - 18 Jul 2025
Viewed by 176
Abstract
To address the challenges of complex low-altitude flight environments for UAVs, where numerous obstacles often lead to GPS signal obstruction and multipath effects, this study proposes an integrated inertial navigation and GPS positioning approach for coordinated cross-path planning in drone swarms. The methodology [...] Read more.
To address the challenges of complex low-altitude flight environments for UAVs, where numerous obstacles often lead to GPS signal obstruction and multipath effects, this study proposes an integrated inertial navigation and GPS positioning approach for coordinated cross-path planning in drone swarms. The methodology involves the following: (1) discretizing continuous 3D airspace into grid cells using occupancy grid mapping to construct an environmental model; (2) analyzing dynamic flight characteristics through attitude angle variations in a 3D Cartesian coordinate system; and (3) implementing collaborative state updates and global positioning through fused inertial–GPS navigation. By incorporating Cramér–Rao lower bound optimization, the system achieves effective cross-path planning for drone formations. Experimental results demonstrate a 98.35% mission success rate with inter-drone navigation time differences maintained below 0.5 s, confirming the method’s effectiveness in enabling synchronized swarm operations while maintaining safe distances during cooperative monitoring and low-altitude flight missions. This approach demonstrates significant advantages in coordinated cross-path planning for UAV clusters. Full article
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20 pages, 741 KiB  
Article
Long-Endurance Collaborative Search and Rescue Based on Maritime Unmanned Systems and Deep-Reinforcement Learning
by Pengyan Dong, Jiahong Liu, Hang Tao, Yang Zhao, Zhijie Feng and Hanjiang Luo
Sensors 2025, 25(13), 4025; https://doi.org/10.3390/s25134025 - 27 Jun 2025
Viewed by 317
Abstract
Maritime vision sensing can be applied to maritime unmanned systems to perform search and rescue (SAR) missions under complex marine environments, as multiple unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) are able to conduct vision sensing through the air, the water-surface, [...] Read more.
Maritime vision sensing can be applied to maritime unmanned systems to perform search and rescue (SAR) missions under complex marine environments, as multiple unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) are able to conduct vision sensing through the air, the water-surface, and underwater. However, in these vision-based maritime SAR systems, collaboration between UAVs and USVs is a critical issue for successful SAR operations. To address this challenge, in this paper, we propose a long-endurance collaborative SAR scheme which exploits the complementary strengths of the maritime unmanned systems. In this scheme, a swarm of UAVs leverages a multi-agent reinforcement-learning (MARL) method and probability maps to perform cooperative first-phase search exploiting UAV’s high altitude and wide field of view of vision sensing. Then, multiple USVs conduct precise real-time second-phase operations by refining the probabilistic map. To deal with the energy constraints of UAVs and perform long-endurance collaborative SAR missions, a multi-USV charging scheduling method is proposed based on MARL to prolong the UAVs’ flight time. Through extensive simulations, the experimental results verified the effectiveness of the proposed scheme and long-endurance search capabilities. Full article
(This article belongs to the Special Issue Underwater Vision Sensing System: 2nd Edition)
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26 pages, 1272 KiB  
Article
Distributed Relative Pose Estimation for Multi-UAV Systems Based on Inertial Navigation and Data Link Fusion
by Kun Li, Shuhui Bu, Jiapeng Li, Zhenyv Xia, Jvboxi Wang and Xiaohan Li
Drones 2025, 9(6), 405; https://doi.org/10.3390/drones9060405 - 30 May 2025
Viewed by 628
Abstract
Accurate self-localization and mutual state estimation are essential for autonomous aerial swarm operations in cooperative exploration, target tracking, and search-and-rescue missions. However, achieving reliable formation positioning in GNSS-denied environments remains a significant challenge. This paper proposes a UAV formation positioning system that integrates [...] Read more.
Accurate self-localization and mutual state estimation are essential for autonomous aerial swarm operations in cooperative exploration, target tracking, and search-and-rescue missions. However, achieving reliable formation positioning in GNSS-denied environments remains a significant challenge. This paper proposes a UAV formation positioning system that integrates inertial navigation with data link-based relative measurements to improve positioning accuracy. Each UAV independently estimates its flight state in real time using onboard IMU data through an inertial navigation fusion method. The estimated states are then transmitted to other UAVs in the formation via a data link, which also provides relative position measurements. Upon receiving data link information, each UAV filters erroneous measurements, time aligns them with its state estimates, and constructs a relative pose optimization factor graph for real-time state estimation. Furthermore, a data selection strategy and a sliding window algorithm are implemented to control data accumulation and mitigate inertial navigation drift. The proposed method is validated through both simulations and real-world two-UAV formation flight experiments. The experimental results demonstrate that the system achieves a 76% reduction in positioning error compared to using data link measurements alone. This approach provides a robust and reliable solution for maintaining precise relative positioning in formation flight without reliance on GNSS. Full article
(This article belongs to the Special Issue Advances in Guidance, Navigation, and Control)
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16 pages, 4397 KiB  
Article
Simulation and Optimization of Multi-Phase Terminal Trajectory for Three-Dimensional Anti-Ship Missiles Based on Hybrid MOPSO
by Jiandong Sun, Shixun You, Di Hua, Zhiwei Xu, Peiyao Wang and Zihang Yang
Algorithms 2025, 18(5), 278; https://doi.org/10.3390/a18050278 - 8 May 2025
Viewed by 599
Abstract
In high-dynamic battlefield environments, anti-ship missiles must perform intricate attitude adjustments and energy management within time constraints to hit a target accurately. Traditional optimization methods face challenges due to the high speed, flexibility, and varied constraints inherent to anti-ship missiles. To overcome these [...] Read more.
In high-dynamic battlefield environments, anti-ship missiles must perform intricate attitude adjustments and energy management within time constraints to hit a target accurately. Traditional optimization methods face challenges due to the high speed, flexibility, and varied constraints inherent to anti-ship missiles. To overcome these challenges, this research introduces a three-dimensional (3D) multi-stage trajectory optimization approach based on the hybrid multi-objective particle swarm optimization algorithm (MOPSO-h). A multi-stage optimization model is developed for terminal trajectory, dividing the flight process into three stages: cruising, altitude adjustment, and penetration dive. Dynamic equations are formulated for each stage, incorporating real-time observations and overload constraints and ensuring the trajectory remains smooth, continuous, and compliant with physical limitations. The proposed algorithm integrates an adaptive hybrid mutation strategy, effectively balancing global search with local exploitation, thus preventing premature convergence. The simulation results demonstrate that, in typical scenarios, the mean miss distance optimized by MOPSO-h remains no greater than 2.34 m, while the terminal landing angle is consistently no less than 85.68°. Furthermore, MOPSO-h enables the missile’s cruise altitude and speed, driven by multiple models, to maintain long-term stability, ensuring that the maneuver overload adheres to physical constraints. This research provides a rigorous and practical solution for anti-ship missile trajectory design and engagement with shipborne air defense systems in high-dynamic environments, achieved through a multi-stage collaborative optimization mechanism and error analysis. Full article
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25 pages, 6985 KiB  
Article
MSCSO: A Modified Sand Cat Swarm Algorithm for 3D UAV Path Planning in Complex Environments with Multiple Threats
by Zhengsheng Zhan, Dangyue Lai, Canjian Huang, Zhixiang Zhang, Yongle Deng and Jian Yang
Sensors 2025, 25(9), 2730; https://doi.org/10.3390/s25092730 - 25 Apr 2025
Viewed by 505
Abstract
To improve the global search efficiency and dynamic adaptability of the Sand Cat Swarm Optimization (SCSO) algorithm for UAV path planning in complex 3D environments, this study proposes a Modified Sand Cat Swarm Optimization (MSCSO) algorithm by integrating chaotic mapping initialization, Lévy flight–Metropolis [...] Read more.
To improve the global search efficiency and dynamic adaptability of the Sand Cat Swarm Optimization (SCSO) algorithm for UAV path planning in complex 3D environments, this study proposes a Modified Sand Cat Swarm Optimization (MSCSO) algorithm by integrating chaotic mapping initialization, Lévy flight–Metropolis hybrid exploration mechanisms, simulated annealing–particle swarm hybrid exploitation strategies, and elite mutation techniques. These strategies not only significantly enhance the convergence speed while ensuring algorithmic precision but also provide effective avenues for enhancing the performance of SCSO. We successfully apply these modifications to UAV path planning scenarios in complex environments. Experimental results on 18 benchmark functions demonstrate the enhanced convergence speed and stability of MSCSO. The proposed method has a superior performance in multimodal optimization tasks. The performance of MSCSO in eight complex scenarios that derived from real-world terrain data by comparing MSCSO with three state-of-the-art algorithms, MSCSO generates shorter average path lengths, reduces collision risks by 21–35%, and achieves higher computational efficiency. Its robustness in obstacle-dense and multi-waypoint environments confirms its practicality in engineering contexts. Overall, MSCSO demonstrates substantial potential in low-altitude resource exploration and emergency rescue operations. These innovative strategies offer theoretical and technical foundations for autonomous decision-making in intelligent unmanned systems. Full article
(This article belongs to the Section Sensors and Robotics)
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32 pages, 7159 KiB  
Article
Grey Wolf Optimization- and Particle Swarm Optimization-Based PD/I Controllers and DC/DC Buck Converters Designed for PEM Fuel Cell-Powered Quadrotor
by Habibe Gursoy Demir
Drones 2025, 9(5), 330; https://doi.org/10.3390/drones9050330 - 24 Apr 2025
Viewed by 525
Abstract
The most important criterion in the design of unmanned air vehicles is to successfully complete the given task and consume minimum energy in the meantime. This paper presents a comparison of the performances of metaheuristic methods such as Particle Swarm Optimization (PSO) and [...] Read more.
The most important criterion in the design of unmanned air vehicles is to successfully complete the given task and consume minimum energy in the meantime. This paper presents a comparison of the performances of metaheuristic methods such as Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to design controllers and DC/DC buck converters for optimizing the energy consumption and path following error of a PEM fuel cell-powered quadrotor system. Hence, the system consists of two PSO- and GWO-based optimizers. Optimizer I is used for determining the parameters of the PD controller, which is used for minimizing the route-tracking error. On the other hand, the I controller parameters and the values of the DC/DC buck converters’ components are determined by Optimizer II to minimize the voltage-tracking errors of the converters. Both optimizers work together in the system and try to minimize tracking errors while also minimizing power consumption by using suitable objective functions. Simulation results demonstrate the effectiveness of the PSO- and GWO-based design of the controllers and converters in enhancing energy efficiency and improving the quadrotor’s flight stability. For step inputs, the GWO-based optimized system shows better performance according to power consumption and the time domain criteria such as rise time and settling time. However, the PSO-based optimized system shows 24.707% better performance for overshoot. On the other hand, 10.8866% less power consumption is observed for the GWO-based optimized system. This power efficient performance of the GWO-based system increases to 18% for the complex route involving ramp and step inputs. Then, a 39 s route test was performed and the total power consumptions for the GWO-based optimized and PSO-based optimized systems were observed to be 168.0015 W/s and 179.9070 W/s, respectively. This means that GWO-based optimizers provide more energy-efficient performance for complex routes. On the other hand, it was determined that the tracking errors in the performance of the desired and actual values of both translational and rotational movement parameters and the forces and torques required for the quadrotor to follow this route were obtained at a maximum of 4% for systems optimized with both techniques. This shows that the full systems optimized with both GWO and PSO algorithms significantly increase their energy efficiency and provide maximum route-following performance. Full article
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14 pages, 4712 KiB  
Article
Nonlinear Hysteresis Parameter Identification of Piezoelectric Actuators Using an Improved Gray Wolf Optimizer with Logistic Chaos Initialization and a Levy Flight Variant
by Yonggang Yan, Kangqiao Duan, Jianjun Cui, Shiwei Guo, Can Cui, Yongsheng Zhou, Junjie Huang, Geng Wang, Dengpan Zhang and Fumin Zhang
Micromachines 2025, 16(5), 492; https://doi.org/10.3390/mi16050492 - 23 Apr 2025
Cited by 1 | Viewed by 381
Abstract
Piezoelectric tilt mirrors are crucial components of precision optical systems. However, the intrinsic hysteretic nonlinearity of the piezoelectric actuator severely restricts the control accuracy of these mirrors and the overall performance of the optical system. This paper proposes an improved Gray Wolf Optimization [...] Read more.
Piezoelectric tilt mirrors are crucial components of precision optical systems. However, the intrinsic hysteretic nonlinearity of the piezoelectric actuator severely restricts the control accuracy of these mirrors and the overall performance of the optical system. This paper proposes an improved Gray Wolf Optimization (GWO) algorithm for high-accuracy identification of hysteresis model parameters based on the Bouc–Wen (BW) differential equation. The proposed algorithm accurately describes the intrinsic hysteretic nonlinear behavior of piezoelectric tilt mirrors. A logistic chaotic mapping method is introduced for population initialization, while a nonlinear convergence factor and a Levy flight strategy are incorporated to enhance global search capabilities during the later stages of optimization. These modifications enable the algorithm to effectively identify BW model parameters for piezoelectric nonlinear systems. Compared to conventional Particle Swarm Optimization (PSO) and standard GWO, the improved algorithm demonstrates faster convergence, higher accuracy, and superior ergodicity, making it a promising tool for solving optimization problems, such as parameter identification in piezoelectric hysteresis systems. This work provides a robust approach for improving the precision and reliability of piezoelectric-driven optical systems. Full article
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17 pages, 428 KiB  
Article
Dynamic UAV Task Allocation and Path Planning with Energy Management Using Adaptive PSO in Rolling Horizon Framework
by Zhen Han and Weian Guo
Appl. Sci. 2025, 15(8), 4220; https://doi.org/10.3390/app15084220 - 11 Apr 2025
Cited by 3 | Viewed by 861
Abstract
Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments for applications such as surveillance, delivery, and data collection, where efficient task allocation and path planning are critical to minimizing mission completion time while managing limited energy resources. This paper proposes a novel [...] Read more.
Unmanned aerial vehicles (UAVs) are increasingly deployed in dynamic environments for applications such as surveillance, delivery, and data collection, where efficient task allocation and path planning are critical to minimizing mission completion time while managing limited energy resources. This paper proposes a novel approach that integrates energy management into a rolling horizon framework for dynamic UAV task allocation and path planning. We introduce an enhanced Particle Swarm Optimization (PSO) algorithm, incorporating adaptive perturbation strategies and a local search mechanism based on simulated annealing, to optimize UAV task assignments and routes. The rolling horizon framework enables the system to adapt to evolving task demands over time. Energy consumption is explicitly modeled, accounting for flight, computation, and recharging at designated stations, ensuring practical applicability. Extensive simulations demonstrate that the proposed method reduces the mission makespan significantly compared to conventional static planning approaches, while effectively balancing energy usage and recharging requirements. These results highlight the potential of our approach for real-world UAV operations in dynamic settings. Full article
(This article belongs to the Topic Electronic Communications, IOT and Big Data, 2nd Volume)
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23 pages, 4329 KiB  
Article
Integrated Aircraft Engine Energy Management Based on Game Theory
by Hong Zhang, Chenyang Luo, Xiangping Li, Runcun Li and Zhilong Fan
Aerospace 2025, 12(4), 328; https://doi.org/10.3390/aerospace12040328 - 10 Apr 2025
Viewed by 1659
Abstract
The current generation of integrated power systems is represented by the Adaptive Power and Thermal Management System (APTMS). The coupled performance between the APTMS and the aircraft engine significantly increases the difficulty of energy management and optimization. This article establishes an energy-coupled Amesim [...] Read more.
The current generation of integrated power systems is represented by the Adaptive Power and Thermal Management System (APTMS). The coupled performance between the APTMS and the aircraft engine significantly increases the difficulty of energy management and optimization. This article establishes an energy-coupled Amesim model of the APTMS and the aircraft engine to analyze performance conflicts. Energy optimization based on the Stackelberg game model is established, with the aircraft engine as the leader and the APTMS as the follower. The Adaptive Chaotic Particle Swarm Optimization (ACPSO) algorithm is introduced to search for the game equilibrium solution. Simulation results indicate that this energy management strategy can achieve equilibrium and alleviate performance conflict. In flight, the optimal strategy depends on thrust–fuel flow characteristics and cooling power demand. Finally, compared with the multi-objective optimization algorithm MOPSO and the non-cooperative Cournot game model, the advantages of this energy management system based on the Stackelberg game are verified. Full article
(This article belongs to the Special Issue Aircraft Design and System Optimization)
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9 pages, 5376 KiB  
Proceeding Paper
Extensible Hook System for Rendezvous and Docking of a CubeSat Swarm
by Carlos Pérez-del-Pulgar, Antonio López Palomeque, Jesús Juli and Matteo Madi
Eng. Proc. 2025, 90(1), 33; https://doi.org/10.3390/engproc2025090033 - 13 Mar 2025
Cited by 1 | Viewed by 266
Abstract
Deployment of CubeSat swarms is proposed for various missions necessitating cooperative interactions among satellites. Commonly, the cube swarm requires formation flight and even rendezvous and docking, which are very challenging tasks since they require more energy and the use of advanced guidance, navigation, [...] Read more.
Deployment of CubeSat swarms is proposed for various missions necessitating cooperative interactions among satellites. Commonly, the cube swarm requires formation flight and even rendezvous and docking, which are very challenging tasks since they require more energy and the use of advanced guidance, navigation, and control techniques. In this paper, we propose the use of an extensible hook system and its corresponding GNC architecture to mitigate these drawbacks, i.e., it allows for saving fuel and reduces system complexity by including techniques that have been previously demonstrated on Earth. This system is based on a scissor boom structure, which could reach up to five meters for a 4U CubeSat dimension, including three degrees of freedom to place the end effector at any pose within the system workspace. We simulated the dynamic behavior of a CubeSat with the proposed system, demonstrating that the required power for a 16U CubeSat equipped with one extensible hook system is considered acceptable according to the current state-of-the-art actuators. Full article
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23 pages, 7956 KiB  
Article
Development of an Improved Jellyfish Search (JS) Algorithm for Solving the Optimal Path Problem of Multi-Robot Collaborative Multi-Tasking in Complex Vertical Farms
by Jiazheng Shen, Saihong Tang, Ruixin Zhao, Luxin Fan, Mohd Khairol Anuar bin Mohd Ariffin and Azizan bin As’arry
Agriculture 2025, 15(6), 578; https://doi.org/10.3390/agriculture15060578 - 9 Mar 2025
Viewed by 827
Abstract
This paper proposes an improved Jellyfish Search algorithm, namely TLDW-JS, for solving the problem of optimal path planning of multi-robot collaboration in the multi-tasking of complex vertical farming environments. Vertical farming is an efficient way to solve the global food problem, but how [...] Read more.
This paper proposes an improved Jellyfish Search algorithm, namely TLDW-JS, for solving the problem of optimal path planning of multi-robot collaboration in the multi-tasking of complex vertical farming environments. Vertical farming is an efficient way to solve the global food problem, but how to deploy agricultural robots in the environment constitutes a great challenge, which involves energy consumption and task efficiency. The most important improvements introduced by the proposed TLDW-JS algorithm are as follows: the Tent Chaos used to generate a high-quality, diversified initial population, Lévy flight used in the improved JS to strengthen global exploration, and finally, the nonlinear dynamically weighted adjustment with logistic functions to balance exploration and exploitation. A Vertical Farming System Multi-Robot Collaborative Trajectory Planning (VFSMRCTP) model has been developed in accordance with the environmental constraints specific to vertical farms, the task constraints, and the constraints between agricultural robots. The VFSMRCTP model is solved using the TLDW-JS algorithm and a number of comparison algorithms in order to analyze the algorithm’s performance. Comparative experiments demonstrate that TLDW-JS outperforms classic optimization algorithms such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Dung Beetle Optimization (DBO), achieving superior path length optimization, reduced energy consumption, and improved convergence speed. The results indicate that TLDW-JS achieved a 34.3% reduction in average path length, obtained one of the top three optimal solutions in 74% of cases, and reached convergence within an average of 55.9 iterations. These results validate the efficiency of TLDW-JS in enhancing energy optimization and demonstrate its potential for enabling automated systems in vertical farming. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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24 pages, 14086 KiB  
Article
Seismic Data Acquisition Utilizing a Group of UAVs
by Artem Timoshenko, Grigoriy Yashin, Valerii Serpiva, Rustam Hamadov, Dmitry Fedotov, Mariia Kartashova and Pavel Golikov
Drones 2025, 9(3), 156; https://doi.org/10.3390/drones9030156 - 20 Feb 2025
Viewed by 3075
Abstract
Seismic exploration in hard-to-reach hazardous environments like deserts is a very expensive and time-consuming process that involves a lot of human resources and equipment. These difficulties can be overcome with the implementation of robots, providing flexible mission design, safe operation, and high precision [...] Read more.
Seismic exploration in hard-to-reach hazardous environments like deserts is a very expensive and time-consuming process that involves a lot of human resources and equipment. These difficulties can be overcome with the implementation of robots, providing flexible mission design, safe operation, and high precision data acquisition. This work presents an autonomous robotic system to assist seismic crews in advanced data acquisition for near-surface characterization, shallow cavity detection, and acquisition grid infill. The developed system consists of a swarm control station and a swarm of unmanned aerial vehicles (UAVs) equipped with seismic sensors. The architecture of the swarm control station, its individual blocks, features of UAV exploitation for seismic data acquisition tasks, hardware and software tool limitations are considered. Algorithms for planning UAV swarm flight paths, their comparison and trajectory examples are presented. Experiments utilizing 9 and 16 UAVs to record 171 and 144 target points, respectively, in harsh desert conditions are described. The results demonstrate the feasibility of the proposed system for seismic data acquisition. The developed robotic system offers flexibility in seismic survey design and planning, enabling efficient coverage of vast areas and facilitating comprehensive data acquisition, which enhances the accuracy and resolution of subsurface seismic imaging. Full article
(This article belongs to the Special Issue Resilient Networking and Task Allocation for Drone Swarms)
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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 943
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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30 pages, 3235 KiB  
Review
Hybrid Renewable Energy Systems—A Review of Optimization Approaches and Future Challenges
by Akvile Giedraityte, Sigitas Rimkevicius, Mantas Marciukaitis, Virginijus Radziukynas and Rimantas Bakas
Appl. Sci. 2025, 15(4), 1744; https://doi.org/10.3390/app15041744 - 8 Feb 2025
Cited by 10 | Viewed by 6318
Abstract
The growing need for sustainable energy solutions has propelled the development of Hybrid Renewable Energy Systems (HRESs), which integrate diverse renewable sources like solar, wind, biomass, geothermal, hydropower and tidal. This review paper focuses on balancing economic, environmental, social and technical criteria to [...] Read more.
The growing need for sustainable energy solutions has propelled the development of Hybrid Renewable Energy Systems (HRESs), which integrate diverse renewable sources like solar, wind, biomass, geothermal, hydropower and tidal. This review paper focuses on balancing economic, environmental, social and technical criteria to enhance system performance and resilience. Using comprehensive methodologies, the review examines state-of-the-art algorithms such as Multi-Objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), alongside Crow Search Algorithm (CSA), Grey Wolf Optimizer (GWO), Levy Flight-Salp Swarm Algorithm (LF-SSA), Mixed-Integer Linear Programming (MILP) and tools like HOMER Pro 3.12–3.16 and MATLAB 9.1–9.13, which have been instrumental in optimizing HRESs. Key findings highlight the growing role of advanced, multi-energy storage technologies in stabilizing HRESs and addressing the intermittency of renewable sources. Moreover, the integration of metaheuristic algorithms with machine learning has enabled dynamic adaptability and predictive optimization, paving the way for real-time energy management. HRES configurations for cost-effectiveness, environmental sustainability, and operational reliability while also emphasizing the transformative potential of emerging technologies such as quantum computing are underscored. This review provides critical insights into the evolving landscape of HRES optimization, offering actionable recommendations for future research and practical applications in achieving global energy sustainability goals. Full article
(This article belongs to the Special Issue Advances in New Sources of Energy and Fuels)
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42 pages, 40649 KiB  
Article
A Multi-Drone System Proof of Concept for Forestry Applications
by André G. Araújo, Carlos A. P. Pizzino, Micael S. Couceiro and Rui P. Rocha
Drones 2025, 9(2), 80; https://doi.org/10.3390/drones9020080 - 21 Jan 2025
Cited by 5 | Viewed by 3280
Abstract
This study presents a multi-drone proof of concept for efficient forest mapping and autonomous operation, framed within the context of the OPENSWARM EU Project. The approach leverages state-of-the-art open-source simultaneous localisation and mapping (SLAM) frameworks, like LiDAR (Light Detection And Ranging) Inertial Odometry [...] Read more.
This study presents a multi-drone proof of concept for efficient forest mapping and autonomous operation, framed within the context of the OPENSWARM EU Project. The approach leverages state-of-the-art open-source simultaneous localisation and mapping (SLAM) frameworks, like LiDAR (Light Detection And Ranging) Inertial Odometry via Smoothing and Mapping (LIO-SAM), and Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm (DCL-SLAM), seamlessly integrated within the MRS UAV System and Swarm Formation packages. This integration is achieved through a series of procedures compliant with Robot Operating System middleware (ROS), including an auto-tuning particle swarm optimisation method for enhanced flight control and stabilisation, which is crucial for autonomous operation in challenging environments. Field experiments conducted in a forest with multiple drones demonstrate the system’s ability to navigate complex terrains as a coordinated swarm, accurately and collaboratively mapping forest areas. Results highlight the potential of this proof of concept, contributing to the development of scalable autonomous solutions for forestry management. The findings emphasise the significance of integrating multiple open-source technologies to advance sustainable forestry practices using swarms of drones. Full article
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