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As the number of complex tasks and applications in Internet of Things (IoT) grows rapidly, the limited computing resources of IoT devices inevitably become insufficient to meet the required quality of service (QoS). Unmanned aerial Vehicle (UAV) -based mobile edge computing (MEC) offers a solution to ensure Qos stability and extend network coverage. In this paper, we consider a multi-UAV aided MEC system and propose an improved particle swarm optimization algorithm based on individual differential evolution and random particle generation (DE-PSO) to address the parameter coupling problem of non-convex objective function. Additionally, we propose a Trajectory Task and Power Two Floor Algorithm (TTPTFA) to minimize the energy expenditure (EE) and user latency through joint optimization of the UAV trajectory, the user and server tasks, and the communication resources. The simulation results show that the TTPTFA algorithm significantly reduces user waiting time compare to the computation uploading method (ToDeTaS) based on UAV deployment, with maintaining the same total computing and data transmission energy expenditure. [ABSTRACT FROM AUTHOR]

This paper presents LEVIOSA, a novel framework for text- and speech-based uncrewed aerial vehicle (UAV) trajectory generation. By leveraging multimodal large language models (LLMs) to interpret natural language commands, the system converts text and audio inputs into executable flight paths for UAV swarms. The approach aims to simplify the complex task of multi-UAV trajectory generation, which has significant applications in fields such as search and rescue, agriculture, infrastructure inspection, and entertainment. The framework involves two key innovations: a multi-critic consensus mechanism to evaluate trajectory quality and a hierarchical prompt structuring for improved task execution. The innovations ensure fidelity to user goals. The framework integrates several multimodal LLMs for high-level planning, converting natural language inputs into 3D waypoints that guide UAV movements and per-UAV low-level controllers to control each UAV in executing its assigned 3D waypoint path based on the high-level plan. The methodology was tested on various trajectory types with promising accuracy, synchronization, and collision avoidance results. The findings pave the way for more intuitive human–robot interactions and advanced multi-UAV coordination. [ABSTRACT FROM AUTHOR]

When the ground communication base stations in the target area are severely destroyed, the deployment of Unmanned Aerial Vehicle (UAV) ad hoc networks can provide people with temporary communication services. Therefore, it is necessary to design a multi-UAVs cooperative control strategy to achieve better communication coverage and lower energy consumption. In this paper, we propose a multi-UAVs coverage model based on Adaptive Virtual Force-directed Particle Swarm Optimization (AVF-PSO) strategy. In particular, we first introduce the gravity model into the traditional Particle Swarm Optimization (PSO) algorithm so as to increase the probability of full coverage. Then, the energy consumption is included in the calculation of the fitness function so that maximum coverage and energy consumption can be balanced. Finally, in order to reduce the communication interference between UAVs, we design an adaptive lift control strategy based on the repulsion model to reduce the repeated coverage of multi-UAVs. Experimental results show that the proposed coverage strategy based on gravity model outperforms the existing state-of-the-art approaches. For example, in the target area of any size, the coverage rate and the repeated coverage rate of the proposed multi-UAVs scheduling are improved by 6.9–29.1%, and 2.0–56.1%, respectively. Moreover, the proposed scheduling algorithm is high adaptable to diverse execution environments.© 2022 Published by Elsevier Ltd.

Multi-UAVs operations encounter significant challenges in conflict avoidance and dynamic path replanning, which are crucial for ensuring safety and mission efficiency. To address these challenges, we propose a novel approach using Mixed Integer Linear Programming (MILP) to optimize UAV flight paths through real-time control of speed and heading angles. Our method formulates conflict resolution as an optimal control problem, aiming to minimize adjustments while satisfying constraints such as minimum separation distances, speed limits, and heading angle limits. The MILP algorithm significantly enhances operational efficiency by reducing conflict-related delays and improving mission completion rates. The effectiveness and practicality of the proposed method are validated by integrating speed and heading angle controls, demonstrating enhanced operational efficiency and cost-effectiveness in complex UAV environments. [ABSTRACT FROM AUTHOR]

Summary: This article looks into the dynamic event‐triggered fixed‐time adaptive attitude control problem for nonlinear six‐rotor unmanned aerial vehicle (UAV) with external disturbances. The multiple six‐rotor UAVs considered are regarded as nonlinear multi‐agent systems (MASs), and each subsystem has multiple inputs. Under the framework of backstepping recursive design, an effective adaptive fixed‐time control method is proposed by combining neural networks (NNs) technology and fixed‐time theory. NNs are utilized to handle unknown nonlinearity and unmodeled parts in attitude systems. The hyperbolic tangent function is ushered to address the singularity problem that may occur in the derivative of the controller, thereby averting the phenomenon of chattering. For the sake of alleviating the correspondence burden of multiple UAVs attitude systems, a modified dynamic event‐triggered mechanism (DETM) is ushered. The developed controller swears for that all signals of the six‐rotor UAV attitude systems are bounded and the tracking errors converge to a small neighborhood of the origin within a fixed‐time interval. Eventually, with the help of simulation results, the effectiveness of the proposed control scheme was verified. [ABSTRACT FROM AUTHOR]

In the past decade, multi-UAV systems have gained significant attention and companies from in various fields. The main aims of growing such systems are able to operate in a coordinated way in complicated situations. To encompass and increase the speed of the applications, this research proposed optimization with intelligence. In the existing work, it does not detect the collision effectively. In this work, the Particle Swarm Optimization algorithm based on fast matching square is proposed for flightpath planning in Multi-UAVs is used. A distributed full coverage optimal route design is derived using the particle swarm optimization (PSO) technique, and a trajectory planner is built utilizing a dynamic fitness function. We applied the PSO technique for every UAV individually to achieve active fitness, maximizing the fitness function while reducing the cost function. The novelty of this work is using the dynamic fitness function for PSO while selecting optimal data. Fast matching square FM2 helps to detect the collision and avoid collisions between the paths. The suggested method generates an optimal path for multi-UAVs and updates the maps for swarms of UAVs. According to experimental findings, the suggested PSO-FM2 outperforms the previous FOA in aspects of convergence and accuracy. Finally, the proposed method is compared with existing algorithms such as MSFOA and PSO. [ABSTRACT FROM AUTHOR]

Combining multiple crop protection Unmanned Aerial Vehicles (UAVs) as a team for a scheduled spraying mission over farmland now is a common way to significantly increase efficiency. However, given some issues such as different configurations, irregular borders, and especially varying pesticide requirements, it is more important and more complex than other multi-Agent Systems (MASs) in common use. In this work, we focus on the mission arrangement of UAVs, which is the foundation of other high-level cooperations, systematically propose Efficiency-first Spraying Mission Arrangement Problem (ESMAP), and try to construct a united problem framework for the mission arrangement of crop protection UAVs. Besides, to characterise the differences in sub-areas, the varying pesticide requirement per unit is well considered based on Normalized Difference Vegetation Index (NDVI). Firstly, the mathematical model of multiple crop-protection UAVs is established and ESMAP is defined. Furthermore, an acquisition method of a farmland’s NDVI map is proposed, and the calculation method of pesticide volume based on NDVI is discussed. Secondly, an improved Genetic Algorithm (GA) is proposed to solve ESMAP, and a comparable combination algorithm is introduced. Numerical simulations for algorithm analysis are carried out within MATLAB, and it is determined that the proposed GA is more efficient and accurate than the latter. Finally, a mission arrangement tested with three UAVs was carried out to validate the effectiveness of the proposed GA in spraying operation. Test results illustrated that it performed well, which took only 90.6 % of the operation time taken by the combination algorithm.

Abstract The Internet of Things (IoT) devices are not able to execute resource-intensive tasks due to their limited storage and computing power. Therefore, Mobile edge computing (MEC) technology has recently been utilized to provide computing and storage capabilities to those devices, enabling them to execute these tasks with less energy consumption and low latency. However, the edge servers in the MEC network are located at fixed positions, which makes them unable to be adjusted according to the requirements of end users. As a result, unmanned aerial vehicles (UAVs) have recently been used to carry the load of these edge servers, making them mobile and capable of meeting the desired requirements for IoT devices. However, the trajectories of the UAVs need to be accurately planned in order to minimize energy consumption for both the IoT devices during data transmission and the UAVs during hovering time and mobility between halting points (HPs). The trajectory planning problem is a complicated optimization problem because it involves several factors that need to be taken into consideration. This problem is considered a multiobjective optimization problem since it requires simultaneous optimization of both the energy consumption of UAVs and that of IoT devices. However, existing algorithms in the literature for this problem have been based on converting it into a single objective, which may give preference to some objectives over others. Therefore, in this study, several multiobjective trajectory planning algorithms (MTPAs) based on various metaheuristic algorithms with variable population size and the Pareto optimality theory are presented. These algorithms aim to optimize both objectives simultaneously. Additionally, a novel mechanism called the cyclic selection mechanism (CSM) is proposed to manage the population size accurately, optimizing the number of HPs and the maximum function evaluations. Furthermore, the HPs estimated by each MTPA are associated with multiple UAVs using the k-means clustering algorithm. Then, a low-complexity greedy mechanism is used to generate the order of HPs assigned to each UAV, determining its trajectory. Several experiments are conducted to assess the effectiveness of the MTPAs with variable population size and cyclic selection mechanisms. The experimental findings demonstrate that the MTPAs with the cyclic selection mechanism outperform all competing algorithms, achieving better outcomes.

In the field of Unmanned Aerial Vehicle (UAV) path planning, designing efficient, safe, and feasible trajectories in complex, dynamic environments poses substantial challenges. Traditional optimization methods often struggle to address the multidimensional nature of these problems, particularly when considering constraints like obstacle avoidance, energy efficiency, and real-time responsiveness. In this paper, we propose a novel algorithm, Dimensional Learning Strategy and Spherical Motion-based Particle Swarm Optimization (DLS-SMPSO), specifically designed to handle the unique constraints and requirements of cooperative path planning for Multiple UAVs (Multi-UAVs). By encoding particle positions as motion paths in spherical coordinates, the algorithm offers a natural and effective approach to navigating multidimensional search spaces. The incorporation of a Dimensional Learning Strategy (DLS) enhances performance by minimizing particle oscillations and allowing each particle to learn valuable information from the global best solution on a dimension-by-dimension basis. Extensive simulations validate the effectiveness of the DLS-SMPSO algorithm, demonstrating its capability to consistently generate optimal paths. The proposed algorithm outperforms other metaheuristic optimization algorithms, achieving a feasibility ratio as high as 97%. The proposed solution is scalable, adaptable, and suitable for real-time implementation, making it an excellent choice for a broad range of cooperative multi-UAV applications.

The saliency feature is a key factor in achieving vision-based tracking for multi-UAV control. However, due to the complex and variable environments encountered during multi-UAV operations—such as changes in lighting conditions and scale variations—the UAV’s visual features may degrade, especially under high-speed movement, ultimately resulting in failure of the vision tracking task and reducing the stability and robustness of swarm flight. Therefore, this paper proposes an adaptive active light source system based on light intensity matching to address the issue of visual feature loss caused by environmental light intensity and scale variations in multi-UAV collaborative navigation. The system consists of three components: an environment sensing and control module, a variable active light source module, and a light source power module. This paper first designs the overall framework of the active light source system, detailing the functions of each module and their collaborative working principles. Furthermore, optimization experiments are conducted on the variable active light source module. By comparing the recognition effects of the variable active light source module under different parameters, the best configuration is selected. In addition, to improve the robustness of the active light source system under different lighting conditions, this paper also constructs a light source color matching model based on light intensity matching. By collecting and comparing visible light images of different color light sources under various intensities and constructing the light intensity matching model using the comprehensive peak signal-to-noise ratio parameter, the model is optimized to ensure the best vision tracking performance under different lighting conditions. Finally, to validate the effectiveness of the proposed active light source system, quantitative and qualitative recognition comparison experiments were conducted in eight different scenarios with UAVs equipped with active light sources. The experimental results show that the UAV equipped with an active light source has improved the recall of yoloV7 and RT-DETR recognition algorithms by 30% and 29.6%, the mAP50 by 21% and 19.5%, and the recognition accuracy by 13.1% and 13.6, respectively. Qualitative experiments also demonstrated that the active light source effectively improved the recognition success rate under low lighting conditions. Extensive qualitative and quantitative experiments confirm that the UAV active light source system based on light intensity matching proposed in this paper effectively enhances the effectiveness and robustness of vision-based tracking for multi-UAVs, particularly in complex and variable environments. This research provides an efficient and computationally effective solution for vision-based multi-UAV systems, further enhancing the visual tracking capabilities of multi-UAVs under complex conditions.

Relative localization (RL) and circumnavigation is a highly challenging problem that is crucial for the safe flight of multi-UAVs (multiple unmanned aerial vehicles). Most methods depend on some external infrastructure for positioning. However, in some complex environments such as forests, it is difficult to set up such infrastructures. In this paper, an approach to infrastructure-free RL estimations of multi-UAVs is investigated for circumnavigating a slowly drifting UGV0 (unmanned ground vehicle 0), where UGV0 serves as the RL and circumnavigation target. Firstly, a discrete-time direct RL estimator is proposed to ascertain the coordinates of each UAV relative to the UGV0 based on intelligent sensing. Secondly, an RL fusion estimation method is proposed to obtain the final estimate of UGV0. Thirdly, an integrated estimation control scheme is also proposed for the application of the RL fusion estimation method to circumnavigation. The convergence and the performance are analyzed. The simulation results validate the effectiveness of the proposed algorithm for RL fusion estimations and of the integrated scheme. [ABSTRACT FROM AUTHOR]

In response to the practice of rescue channel blocking and a shortage of emergency materials in the event of sudden significant disasters, a multi-UAV collaborative distribution scheme was designed based on the demand for rapid and accurate distribution of materials. This paper constructed a multi-UAV collaborative task assignment and routing problem with simultaneous delivery and pick-up and time windows (MVTARPSDPTW), considering the factors of UAV load, energy consumption, cargo quality, and volume to minimize the total cost of UAV distribution and the full penalty of the task, as well as optimizing the balance of UAV efficiency. This paper proposes a multi-objective, improved brainstorming optimization algorithm based on Pareto dominance (MIBSO) to solve the MVTARPSDPTW problem. With DTLZ4, DTLZ5, and DTLZ6 benchmarks, this work tests the algorithm performance according to the characteristic attributes of the model sought, selecting the four indicators of GD, the Spacing metric, HV, and IGD, concerning convergence, solution distribution, and comprehensive performance. Case validation is based on a COVID-19 scenario in Changchun, China, and the results show that the model algorithm designed in this paper has good performance and feasibility in convergence and distribution of reconciliation. Finally, the multi-UAV emergency material distribution solution provides practical, theoretical support for rescue tasks in sudden significant disasters. [ABSTRACT FROM AUTHOR]

Fixed-wing unmanned aerial vehicles (UAVs) possess high speed and non-hovering capabilities, rendering them uniquely advantages for reconnaissance and detection. The focus of this paper is to addressing the problem of formation control for fixed-wing UAVs in the presence of communication delay. To tackle this problem, for the non-holonomic kinematic model, we propose an intuitive and practical control law based on the leader-follower method to ensure that UAVs maintain a predetermined geometric formation. The stability analysis of the system with communication delay is conducted by constructing a strict Lyapunov–Krasovskii function. Furthermore, we consider the impact of communication delay on formation accuracy and present a prediction algorithm capable of forecasting the actual position of each UAV. To validate our theoretical findings, both digital simulation and hardware-in-loop experiment are conducted. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) devices are not able to execute resource-intensive tasks due to their limited storage and computing power. Therefore, Mobile edge computing (MEC) technology has recently been utilized to provide computing and storage capabilities to those devices, enabling them to execute these tasks with less energy consumption and low latency. However, the edge servers in the MEC network are located at fixed positions, which makes them unable to be adjusted according to the requirements of end users. As a result, unmanned aerial vehicles (UAVs) have recently been used to carry the load of these edge servers, making them mobile and capable of meeting the desired requirements for IoT devices. However, the trajectories of the UAVs need to be accurately planned in order to minimize energy consumption for both the IoT devices during data transmission and the UAVs during hovering time and mobility between halting points (HPs). The trajectory planning problem is a complicated optimization problem because it involves several factors that need to be taken into consideration. This problem is considered a multiobjective optimization problem since it requires simultaneous optimization of both the energy consumption of UAVs and that of IoT devices. However, existing algorithms in the literature for this problem have been based on converting it into a single objective, which may give preference to some objectives over others. Therefore, in this study, several multiobjective trajectory planning algorithms (MTPAs) based on various metaheuristic algorithms with variable population size and the Pareto optimality theory are presented. These algorithms aim to optimize both objectives simultaneously. Additionally, a novel mechanism called the cyclic selection mechanism (CSM) is proposed to manage the population size accurately, optimizing the number of HPs and the maximum function evaluations. Furthermore, the HPs estimated by each MTPA are associated with multiple UAVs using the k-means clustering algorithm. Then, a low-complexity greedy mechanism is used to generate the order of HPs assigned to each UAV, determining its trajectory. Several experiments are conducted to assess the effectiveness of the MTPAs with variable population size and cyclic selection mechanisms. The experimental findings demonstrate that the MTPAs with the cyclic selection mechanism outperform all competing algorithms, achieving better outcomes. [ABSTRACT FROM AUTHOR]

In this paper, we undertake an examination of the complicated challenges associated with bandwidth allocation and power control in multi- unmanned aerial vehicles (UAVs) network. The focal point of our investigation is the formulation and subsequent proposition of a novel algorithm aimed at optimizing bandwidth utilization and energy efficiency across multiple UAVs. The intricate joint optimization problem is cast as a nontrivial nonlinear programming (NLP) challenge; thus, to efficiently address it, we split it into two subproblems. In the first sub-problem, UAVs bandwidth resources are being optimized while considering that they are operating at their maximum transmit powers. In the second sub-problem and after optimizing bandwidth allocations, UAVs transmit powers are optimized based on their inter-links speed. Sequential quadratic programing (SQP) based on coloring graph representation is proposed to address the first sub-problem, while $\mathcal {M}$ - matrix theory is employed to address the second one. Rigorous numerical simulations are conducted to prove the effectiveness of the proposed scheme against other benchmarks in maximizing both data rate and energy efficiency of the proposed multi-UAV network.

Aiming at the cooperative pursuit and confrontation task of multiple UAVs in the unknown escape UAV environment, a multi-UAVs cooperative pursuit and confrontation strategy based on P3C-MADDPG algorithm is proposed. First, in order to solve the problem of slow training speed and over estimation of Q value of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, In MADDPG algorithm, Prioritized Experience Replay (PER) based on tree structure storage and a parallel Critic network model with 3 threads are prioritized respectively, and the P3C-MADDPG algorithm is proposed. Then, based on the kinematics model of UAV, training elements such as state space, reward function combining sparse reward and guided reward, pursuit action space with different accelerations are designed. Finally, based on the above training elements, the P3C-MADDPG algorithm is used to generate the cooperative pursuit and confrontation strategy of multiple UAVs in the unknown escape UAV environment. Simulation experiments show that the P3C-MADDPG algorithm increases the training speed by 11.7% on average, and decreases the Q value by 6.06% on average. The generated multi-UAV cooperative pursuit and confrontation strategy can effectively avoid obstacles, and more intelligently realize the pursuit of unmanned aerial vehicles with unknown strategies.

In the task assignment problem of multi-UAV collaborative reconnaissance, existing algorithms have issues with inadequate solution accuracy, specifically manifested as large spatial spans and knots of routes in the task execution of UAVs. To address the above challenges, this paper presents a multi-UAV task assignment model under complex conditions (MTAMCC). To efficiently solve this model, this paper proposes an elite wolf pack algorithm based on probability threshold (EWPA-PT). The EWPA-PT algorithm combines the wandering behavior in the traditional wolf pack algorithm with the genetic algorithm. It introduces an ordered permutation problem to calculate the adaptive wandering times of the detective wolves in a specific direction. During the calling phase of the algorithm, the fierce wolves in the wolf pack randomly learn the task assignment results of the head wolf. The sieging behavior introduces the Metropolis criterion from the simulated annealing algorithm to replace the distance threshold in traditional wolf pack algorithms with a probability threshold, which dynamically changes during the iteration process. The wolf pack updating mechanism leverages the task assignment experience of the elite group to reconstruct individual wolves, thereby improving the individual reconstruction’s efficiency. Experiments demonstrate that the EWPA-PT algorithm significantly improves solution accuracy compared to typical methods in recent years.

Due to the swift development of the Internet of Things (IoT), massive advanced terminals such as sensor nodes have been deployed across diverse applications to sense and acquire surrounding data. Given their limited onboard capabilities, these terminals tend to offload data to servers for further processing. However, terminals cannot transmit data directly in regions with restricted communication infrastructure. With the increasing proliferation of unmanned aerial vehicles (UAVs), they have become instrumental in collecting and transmitting data from the region to servers. Nevertheless, because of the energy constraints and time-consuming nature of data processing by UAVs, it becomes imperative not only to utilize multiple UAVs to traverse a large-scale region and collect data, but also to overcome the substantial challenge posed by the time sensitivity of data information. Therefore, this paper introduces the important indicator Age of Information (AoI) that measures data freshness, and develops an intelligent AoI optimization data processing approach named AODP in a hierarchical cloud–edge architecture. In the proposed AODP, we design a management mechanism through the formation of clusters by terminals and the service associations between terminals and hovering positions (HPs). To further improve collection efficiency of UAVs, an HP clustering strategy is developed to construct the UAV-HP association. Finally, under the consideration of energy supply, time tolerance, and flexible computing modes, a gray wolf optimization algorithm-based multi-objective path planning scheme is proposed, achieving both average and peak AoI minimization. Simulation results demonstrate that the AODP can converge well, guarantee reliable AoI, and exhibit superior performance compared to existing solutions in multiple scenarios.

Multiple unmanned aerial vehicles (multi-UAVs) formation shape refers to the geometric shape when multi-UAVs fly in formation and describes their relative positions. It plays a necessary role in multi-UAVs collaboration to improve performance, avoid collision, and provide reference for control. This study aims to determine the most appropriate multi-UAVs formation shape in a specific mission to meet different and even conflicting requirements. The proposed approach introduces requirement satisfaction and spherical fuzzy analytic network process (SFANP) to improve the technique for order preference by similarity to ideal solution (TOPSIS). First, multi-UAVs capability criteria and their evaluation models are constructed. Next, performance data are transformed into requirement satisfaction of capability and unified into a same scale. Qualitative judgments are made and quantified based on spherical fuzzy sets and nonlinear transformation functions are developed for benefit, cost, and interval metrics. Then, SFANP is used to handle interrelationships among criteria and determine their global weights, which takes decision vagueness and hesitancy into account and extends decision-makers' preference domain onto a spherical surface. Finally, alternative formation shapes are ranked by their distances to the positive and negative ideal solution according to the TOPSIS. Furthermore, a case study of 9 UAVs performing a search-attack mission is set up to illustrate the proposed approach, and a comparative analysis is conducted to verify the applicability and credibility. [ABSTRACT FROM AUTHOR]

Motivated by feedback from firefighters in Normandy, this work aims to provide a simple technique for a set of identical drones to collectively describe an arbitrary planar virtual shape in a 3D space in a decentralized manner. The original problem involved surrounding a toxic cloud to monitor its composition and short-term evolution. In the present work, the pattern is described using Fourier descriptors, a convenient mathematical formulation for that purpose. Starting from a reference point, which can be the center of a fire, Fourier descriptors allow for more precise description of a shape as the number of harmonics increases. This pattern needs to be evenly occupied by the fleet of drones under consideration. To optimize the overall view, the drones must be evenly distributed angularly along the shape. The proposed method enables virtual planar shape description, decentralized bearing angle assignment, drone movement from takeoff positions to locations along the shape, and collision avoidance. Furthermore, the method allows for the number of drones to change during the mission. The method has been tested both in simulation, through emulation, and in outdoor experiments with real drones. The obtained results demonstrate that the method is applicable in real-world contexts. [ABSTRACT FROM AUTHOR]

Copyright of Command Control & Simulation / Zhihui Kongzhi yu Fangzhen is the property of Command Control & Simulation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Summary: This article studies the memory‐based dynamic event‐triggered (MBDET) robust adaptive fuzzy control problem for a class of uncertain nonlinear MASs subject to Backlash‐Like Hysteresis. The considered MASs are modeled as a class of nonlinear leader‐following multiagent systems with multiple inputs for each subsystem, which can be used to model multiple multi‐rotor unmanned aerial vehicle (Multi‐UAV) attitude systems. In the framework of backstepping recursive design, the fuzzy approximation technology, the parameter estimation technology, and the first‐order filtering technology are integrated, and an effective fuzzy adaptive control method is proposed, which solves the problems of Backlash‐Like Hysteresis and external disturbances in the system while avoiding the "explosion of complexity". In order to relieve the communication pressure of Multi‐UAV attitude systems, a novel dynamic memory‐event‐triggered mechanism (DMETM) based on user‐set parameters is developed. Compared with the traditional event‐triggered mechanism, this DMETM has larger triggering time intervals, effectively reduces communication frequency, and has greater flexibility in balancing system performance and communication bandwidth resource constraints. The closed‐loop stability analysis scheme for Multi‐UAV attitude systems is presented by combining the backstepping recursive design technique and Lyapunov stability theory, which proves that closed‐loop stability and tracking performance can be ensured. Finally, the design method is successfully applied to show the effectiveness of the proposed method to solve the dynamic memory‐event‐triggered attitude control problem for nonlinear Multi‐UAVs. [ABSTRACT FROM AUTHOR]

Multi-UAV systems have been widely used in reconnaissance, disaster relief, communication, and other fields. However, many dynamic events can cause a partial failure of the original mission during the mission execution process, in which case task reassignment should be carried out. How to reassign resources and tasks in multi-dynamic, multi-target, and multi-constraint events becomes a core issue in the enhancement of combat efficiency. This paper establishes a model of multi-UAV cooperative reconnaissance task reassignment that comprehensively considers various dynamic factors such as UAV performance differences, size of target areas, and time window constraints. Then, a two-stage distributed task assignment algorithm (TS-DTA) is presented to achieve multi-task reassignment in dynamic environments. Finally, this paper verifies the effectiveness of the TS-DTA algorithm through simulation experiments and analyzes its performance through comparative experiments. The experimental results show that the TS-DTA algorithm can efficiently solve the task reassignment problem in dynamic environments while effectively reducing the communication burden of UAV formations. [ABSTRACT FROM AUTHOR]

In this paper, a multiple unmanned aerial vehicles (multi-UAVs) cooperative target search and tracking strategy with collision avoidance and flocking constraints are presented applying the control barrier functions (CBFs). First, the constraints of collision avoidance and flocking maintenance are designed based on CBF. Second, the information map and Lyapunov guidance vector field (LGVF) are used to design the corresponding nominal controllers in search mode and tracking mode, respectively. To minimize the difference between the actual controller and the nominal controller, the controller based on quadratic programming (QP) is proposed to achieve the target search and tracking on the premise of security. Finally, the simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of National University of Defense Technology / Guofang Keji Daxue Xuebao is the property of NUDT Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper concerns the multi-UAV task assignment problem, which is solved by a multi-objective particle swarm optimization algorithm for adaptive region partitioning. Since the traditional multi-objective optimization algorithms tend to fall into local optimum solutions when dealing with optimization problems, this paper establishes an improved multi-objective particle swarm optimization (MOPSO) algorithm based on the adaptive angle area division. This paper proposes a new multi-UAV task assignment model where the threat constraint is concerned. To solve this model, the algorithm first preprocesses solution spatial information, including normalization of solutions and area division of space. Further, global optimal particle selection strategy is improved based on angle of division. In order to improve the global searching ability, some infeasible solution is used. Finally in the implementation stage of the algorithm, we set multiple nodes for the trajectory of the UAVs to increase the stability of the algorithm. The simulation experiments results demonstrate that the improved algorithm can provide a flyable solution for the UAVs and achieve better convergence and diversity.

In response to the practice of rescue channel blocking and a shortage of emergency materials in the event of sudden significant disasters, a multi-UAV collaborative distribution scheme was designed based on the demand for rapid and accurate distribution of materials. This paper constructed a multi-UAV collaborative task assignment and routing problem with simultaneous delivery and pick-up and time windows (MVTARPSDPTW), considering the factors of UAV load, energy consumption, cargo quality, and volume to minimize the total cost of UAV distribution and the full penalty of the task, as well as optimizing the balance of UAV efficiency. This paper proposes a multi-objective, improved brainstorming optimization algorithm based on Pareto dominance (MIBSO) to solve the MVTARPSDPTW problem. With DTLZ4, DTLZ5, and DTLZ6 benchmarks, this work tests the algorithm performance according to the characteristic attributes of the model sought, selecting the four indicators of GD, the Spacing metric, HV, and IGD, concerning convergence, solution distribution, and comprehensive performance. Case validation is based on a COVID-19 scenario in Changchun, China, and the results show that the model algorithm designed in this paper has good performance and feasibility in convergence and distribution of reconciliation. Finally, the multi-UAV emergency material distribution solution provides practical, theoretical support for rescue tasks in sudden significant disasters.

UAVs formation is a complex nonlinear system. Although quadrotor has many advantages, its dynamics model is nonlinear, coupled, and under driven. For model-based controller design, physical constraints, model uncertainties and external disturbances significantly deteriorate control performance. Therefore, a multi-constraints MPC strategy based on error model is proposed for the quadrotor formation. Firstly, a 3D model for quadrotor is established by using the Lagrange-Euler formulation, in which the model is divided into RS and TS. The corresponding multi-constraint MPC is designed for the two subsystems respectively. The tracking errors caused by external disturbances can be eliminated because of the integrator embedded in the augmented model. Comparing with RMPC, by making reasonable modifications to the cost function, the asymptotic stability of the open loop and the closed-loop subsystem can be ensured by MMPC. Moreover, the stability analysis of the above-mentioned algorithm is carried out. The simulation results show that the controller of the formation can not only achieve good path tracking, but also robustness to the multiple constraints and disturbances.

With the gradual establishment of regional cooperative air defense systems by the world’s military powers, the success rate of a single-aircraft penetration operation is greatly reduced, and the concept of many-to-one cooperative operation has been widely valued. As a new type of lethal aerial weapon, suicide unmanned aerial vehicles (UAVs) have played an important role in many local wars recently. Compared with traditional missiles, suicide UAVs can hover in a combat area for a long time, waiting for potential targets. Moreover, a suicide UAV cannot be easily detected via an early warning system and can approach targets covertly. Further, the manufacturing cost of a suicide UAV is low, and it can form a large-scale swarm for a surprise attack. Therefore, in the foreseeable future, a multi-UAV cooperative attack is likely to subvert existing combat styles. According to the operational characteristics and requirements of multi-UAV cooperative attacks, a general guidance scheme for the cooperative attack of multi-UAVs is proposed. Based on the theory that proportional navigation law has trajectory uniqueness under specific variable constraints, the guidance phase is divided into coordination and terminal phases by selecting coordinated variables. The improved Dubins method is used in the track control of the coordination phase to realize the space–time synchronous convergence of coordination variables. The 3D space guidance is decoupled into longitudinal- and lateral-plane guidance in the terminal phase, and the impact time of the swarm is consistent based on the proportional guidance with the same coefficient. A track segment control realizes the space–time cooperation of the swarm considering the target defense range constraint. Numerical simulation and actual flight test results show that the scheme has real-time online planning ability, can realize an omnidirectional saturation attack under the space–time cooperation of a large-scale UAV swarm, and has high impact time and space precision.

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Though space travel is technologically developed, bad weather makes path planning inefficient. When an unmanned aerial vehicle looks for an optimal path, it is hard to carry out a mission from unidentified obstacles. In the existing method, the path planner does not work with multiple UAVs. Also path optimization is considered as a big challenge for multiple UAV movement. The proposed work, uses Multiple Swarm Fruit fly optimization with the Q-Learning method for path planning in multiple UAVs. The proposed MSFOA divides the total fruit fly swarms into sub-swarms with multi-job also improves the searching area. In addition, the Q Learning-based path selection strategy is used to optimize global and local searches during the evolutionary process. Whereas the offspring competitive method is used to enhance the level of use of each computation result and to facilitate the transmission of information across different fruit fly sub-swarms. The proposed method MSFO-QL for path planning reduces costs and finds the path more efficiently. Computational results show that the suggested MSFO-QL can handle the limited UAV path planning with minimum worst selection with 280 on case 1 and 232 on case 2. Proposed is more efficient and robust than the existing optimization techniques. [ABSTRACT FROM AUTHOR]

This paper presents a Multiple Unmanned Aerial Vehicles (UAVs) cooperative reconnaissance task allocation model based on heterogeneous target value and proposes an improved Multi-Verse Optimizer (MVO) algorithm. Firstly, according to the reconnaissance value of the target, the reconnaissance targets are divided into high-value targets, low-value targets and decoy targets. It improves the authenticity of the problem. The purpose of task allocation is to maximize the reconnaissance revenue of UAVs as much as possible under the condition of minimizing the reconnaissance time and fuel loss of UAVs to the targets. Then, to solve the model, this paper improves the traditional MVO algorithm. Adaptive compression factor is introduced to improve the convergence speed of the algorithm. In addition, the differential mutation operation is performed in the wormhole movement stage to enhance the global search ability of the algorithm. The simulation results show that the improved algorithm can successfully solve the reconnaissance task allocation problem under different target values, and has obvious advantages in reconnaissance revenue and calculation speed compared with other methods.