Multi-UAV Collaborative Mission Planning Method for Self-Organized Sensor Data Acquisition

In recent years,sensor technology has been widely used in the defense and control of sensitive areas in cities,or in various scenarios such as early warning of forest fires,monitoring of forest pests and diseases,and protection of endangered animals.Deploying sensors to collect data and then utilizing unmanned aerial vehicle(UAV)to collect the data stored in the sensors has replaced traditional manual data collection as the dominant method.The current strategies for efficient data collection in above scenarios are still imperfect,and the low quality of the collected data and the excessive energy consumed by UAV flights are still the main problems faced in data collection.With regards this,this paper proposes a multi-UAV mission planning method for self-organized sensor data acquisition by comprehensively utilizing the techniques of self-organized sensor clustering,multi-UAV mission area allocation,and sub-area data acquisition scheme optimization.The improvedα-hop clustering method utilizes the average transmission distance to reduce the size of the collection sensors,and the K-Dimensional method is used to form a multi-UAV cooperative workspace,and then,the genetic algorithm is used to trade-off the speed with the age of information(AoI)of the collected information and the energy consumption to form the multi-UAV data collection operation scheme.The combined optimization scheme in paper improves the performance by 95.56%and 58.21%,respectively,compared to the traditional baseline model.In order to verify the excellent generalization and applicability of the proposed method in real scenarios,the simulation test is conducted by introducing the digital elevation model data of the real terrain,and the results show that the relative error values of the proposed method and the performance test of the actual flight of the UAV are within the error interval of±10%.Then,the advantages and disadvantages of the present method with the existing mainstream schemes are tested,and the results show that the present method has a huge advantage in terms of space and time complexity,and at the same time,the accuracy for data extraction is relatively improved by 10.46%and 12.71%.Finally,by eliminating the clustering process and the subtask assignment process,the AoI performance decreases by 3.46×and 4.45×,and the energy performance decreases by 3.52×and 4.47×.This paper presents a comprehensive and detailed proactive optimization of the existing challenges faced in the field of data acquisition by means of a series of combinatorial optimizations.

Resilient Time-Varying Formation-Tracking of Multi-UAV Systems Against Composite Attacks: A Two-Layered Framework

This paper studies the countermeasure design problems of distributed resilient time-varying formation-tracking control for multi-UAV systems with single-way communications against composite attacks,including denial-of-services(DoS)attacks,false-data injection attacks,camouflage attacks,and actuation attacks(AAs).Inspired by the concept of digital twin,a new two-layered protocol equipped with a safe and private twin layer(TL)is proposed,which decouples the above problems into the defense scheme against DoS attacks on the TL and the defense scheme against AAs on the cyber-physical layer.First,a topologyrepairing strategy against frequency-constrained DoS attacks is implemented via a Zeno-free event-triggered estimation scheme,which saves communication resources considerably.The upper bound of the reaction time needed to launch the repaired topology after the occurrence of DoS attacks is calculated.Second,a decentralized adaptive and chattering-relief controller against potentially unbounded AAs is designed.Moreover,this novel adaptive controller can achieve uniformly ultimately bounded convergence,whose error bound can be given explicitly.The practicability and validity of this new two-layered protocol are shown via a simulation example and a UAV swarm experiment equipped with both Ultra-WideBand and WiFi communication channels.

Max-Min Adaptive Ant Colony Optimization Approach to Multi-UAVs Coordinated Trajectory Replanning in Dynamic and Uncertain Environments

Multiple Uninhabited Aerial Vehicles (multi-UAVs) coordinated trajectory replanning is one of the most complicated global optimum problems in multi-UAVs coordinated control. Based on the construction of the basic model of multi-UAVs coordinated trajectory replanning, which includes problem description, threat modeling, constraint conditions, coordinated function and coordination mechanism, a novel Max-Min adaptive Ant Colony Optimization (ACO) approach is presented in detail. In view of the characteristics of multi-UAVs coordinated trajectory replanning in dynamic and uncertain environments, the minimum and maximum pheromone trails in ACO are set to enhance the searching capability, and the point pheromone is adopted to achieve the collision avoidance between UAVs at the trajectory planner layer. Considering the simultaneous arrival and the air-space collision avoidance, an Estimated Time of Arrival (ETA) is decided first. Then the trajectory and flight velocity of each UAV are determined. Simulation experiments are performed under the complicated combating environment containing some static threats and popup threats. The results demonstrate the feasibility and the effectiveness of the proposed approach.

Energy-Efficient Multi-UAV Coverage Deployment in UAV Networks:A Game-Theoretic Framework

UAV cooperative control has been applied in many complex UAV communication networks. It remains challenging to develop UAV cooperative coverage and UAV energy-efficient communication technology. In this paper, we investigate current works about UAV coverage problem and propose a multi-UAV coverage model based on energy-efficient communication. The proposed model is decomposed into two steps: coverage maximization and power control, both are proved to be exact potential games(EPG) and have Nash equilibrium(NE) points. Then the multi-UAV energy-efficient coverage deployment algorithm based on spatial adaptive play(MUECD-SAP) is adopted to perform coverage maximization and power control, which guarantees optimal energy-efficient coverage deployment. Finally, simulation results show the effectiveness of our proposed approach, and confirm the reliability of proposed model.

Joint Subcarrier and Power Allocation for Multi-UAV Systems

This paper investigates subcarrier and power allocation in a multi-UAV OFDM system.The study considers a practical scenario,where certain subcarriers are unavailable for dynamic subcarrier allocation,on account of pre-allocation for burst transmissions.We first propose a novel iterative algorithm to jointly optimize subcarrier and power allocation,so as to maximize the sum rate of the uplink transmission in the multiUAV OFDM system.The key idea behind our solution is converting the nontrivial allocation problem into a weighted mean square error(MSE) problem.By this means,the allocation problem can be solved by the alternating optimization method.Besides,aiming at a lower-complexity solution,we propose a heuristic allocation scheme,where subcarrier allocation and transmit power allocation are separately optimized.In the heuristic scheme,closedform solution can be obtained for power allocation.Simulation results demonstrate that in the presence of stretched subcarrier resource,the proposed iterative joint optimization algorithm can significantly outperform the heuristic scheme,offering a higher sum rate.

Multi-UAV Network Assisted Intelligent Edge Computing:Challenges and Opportunities

Introducing multi-UAV network with flexible deployment into mobile edge computing(MEC)can effectively improve the quality of service of Internet-of-Things services,reduce the coverage cost and resource waste rate of edge nodes,and also bring some challenges.This paper first introduces the current situation and pain points of mobile edge computing,then analyzes the significance and value of using multi-UAV network to assist mobile edge computing,and summarizes its key technologies and typical applications.In the end,some open research problems and technology prospects of multi-UAV network assisted intelligent edge computing are put forward,which provide new ideas for the future development of this field.

Intelligent Task Offloading and Collaborative Computation in Multi-UAV-Enabled Mobile Edge Computing

This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions,while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution(DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network(DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity.

Robust Trajectory and Communication Design for Angle-Constrained Multi-UAV Communications in the Presence of Jammers

This paper studies a multi-unmanned aerial vehicle(UAV)enabled wireless communication system,where multiple UAVs are employed to communicate with a group of ground terminals(GTs)in the presence of potential jammers.We aim to maximize the throughput overall GTs by jointly optimizing the UAVs’trajectory,the GTs’scheduling and power allocation.Unlike most prior studies,we consider the UAVs’turning and climbing angle constraints,the UAVs’three-dimensional(3D)trajectory constraints,minimum UAV-to-UAV(U2U)distance constraint,and the GTs’transmit power requirements.However,the formulated problem is a mixed-integer non-convex problem and is intractable to work it out with conventional optimization methods.To tackle this difficulty,we propose an efficient robust iterative algorithm to decompose the original problem be three sub-problems and acquire the suboptimal solution via utilizing the block coordinate descent(BCD)method,successive convex approximation(SCA)technique,and S-procedure.Extensive simulation results show that our proposed robust iterative algorithm offers a substantial gain in the system performance compared with the benchmark algorithms.

Adaptive Cascaded High-Resolution Source Localization Based on Collaboration of Multi-UAVs

Effective information fusion is very important in hybrid source localization. In this paper, the performance analysis of conventional joint direction of arrival(DOA) and time difference of arrival(TDOA) system is derived and it is shown that this hybrid system may inferior to the single system when the ratio of angular measurements error to distance measurements error exceeds a threshold. To avoid this problem, an effective DOA/TDOA adaptive cascaded(DTAC) technique is presented. The rotation feature of UAVs and spatial filtering technique are applied to gain the signal-to-noise ratio(SNR), which leads to more accurate estimation of time delay by using DOAs. Nevertheless, the time delay estimation precision is still limited by the sampling frequency, which is constrained by the finite load of UAV. To break through the limitation, an enhanced self-delay-compensation(SDC) method is proposed, which aims at detecting the overlooked time delay within the sampling interval by adding a tiny time delay. Finally, the position of the source is estimated by the Chan algorithm. Compared to DOA-only algorithm, TDOA-only algorithm and joint DOA/TDOA(JDT) algorithm, the proposed method shows better localization accuracy regardless of different SNRs and sampling frequencies. Numerical simulations are presented to validate the effectiveness and robustness of the proposed algorithm.

Multi-UAV Cooperative GPS Spoofing Based on YOLO Nano

In recent years,with the rapid development of the drone industry,drones have been widely used in many fields such as aerial photography,plant protection,performance,and monitoring.To effectively control the unauthorized flight of drones,using GPS spoofing attacks to interfere with the flight of drones is a relatively simple and highly feasible attack method.However,the current method uses ground equipment to carry out spoofing attacks.The attack range is limited and the flexibility is not high.Based on the existing methods,this paper proposes a multi-UAV coordinated GPS spoofing scheme based on YOLO Nano,which can launch effective attacks against target drones with autonomous movement:First,a single-attack drone based on YOLO Nano is proposed.The target tracking scheme achieves accurate tracking of the target direction on a single-attack drone;then,based on the single-UAV target tracking,a multi-attack drone coordinated target tracking scheme based on the weighted least squares method is proposed to realize the target drone Finally,a new calculation method for false GPS signals is proposed,which adaptively adjusts the flight trajectory of the attacking drone and the content of the false GPS signal according to the autonomous movement of the target drone.

Path planning method for controlling multi-UAVs to reach multi-waypoints simultaneously under the view of visual navigation

Abstract： There is a high demand for unmanned aerial vehicle （UAV） flight stability when using vi- sion as a detection method for navigation control. To meet such demand, a new path planning meth- od for controlling multi-UAVs is studied to reach multi-waypoints simultaneously under the view of visual navigation technology. A model based on the stable-shortest pythagorean-hodograph （PH） curve is established, which could not only satisfy the demands of visual navigation and control law, but also be easy to compute. Based on the model, a planning algorithm to guide multi-UAVs to reach multi-waypoints at the same time without collisions is developed. The simulation results show that the paths have shorter distance and smaller curvature than traditional methods, which could help to avoid collisions.

Dynamic Multi-team Antagonistic Games Model with Incomplete Information and Its Application to Multi-UAV

At present, the studies on multi-team antagonistic games (MTAGs) are still in the early stage, because this complicated problem involves not only incompleteness of information and conflict of interests, but also selection of antagonistic targets. Therefore, based on the previous researches, a new framework is proposed in this paper, which is dynamic multi-team antagonistic games with incomplete information (DMTAGII) model. For this model, the corresponding concept of perfect Bayesian Nash equilibrium (PBNE) is established and the existence of PBNE is also proved. Besides, an interactive iteration algorithm is introduced according to the idea of the best response for solving the equilibrium. Then, the scenario of multiple unmanned aerial vehicles (UAVs) against multiple military targets is studied to solve the problems of tactical decision making based on the DMTAGII model. In the process of modeling, the specific expressions of strategy, status and payoff functions of the games are considered, and the strategy is coded to match the structure of genetic algorithm so that the PBNE can be solved by combining the genetic algorithm and the interactive iteration algorithm. Finally, through the simulation the feasibility and effectiveness of the DMTAGII model are verified. Meanwhile, the calculated equilibrium strategies are also found to be realistic, which can provide certain references for improving the autonomous ability of UAV systems. © 2014 Chinese Association of Automation.

An attack-resilient distributed extended Kalman consensus filtering algorithm with applications to multi-UAV tracking problems

This study investigates how the events of deception attacks are distributed during the fusion of multi-sensor nonlinear systems.First,a deception attack with limited energy(DALE)is introduced under the framework of distributed extended Kalman consensus filtering(DEKCF).Next,a hypothesis testing-based mechanism to detect the abnormal data generated by DALE,in the presence of the error term caused by the linearization of the nonlinear system,is established.Once the DALE is detected,a new rectification strategy can be triggered to recalibrate the abnormal data,restoring it to its normal state.Then,an attack-resilient DEKCF(AR-DEKCF)algorithm is proposed,and its fusion estimation errors are demonstrated to satisfy the mean square exponential boundedness performance,under appropriate conditions.Finally,the effectiveness of the AR-DEKCF algorithm is confirmed through simulations involving multi-unmanned aerial vehicle(multi-UAV)tracking problems.

Elite Dung Beetle Optimization Algorithm for Multi-UAV Cooperative Search in Mountainous Environments

This paper aims to address the problem of multi-UAV cooperative search for multiple targets in a mountainous environment,considering the constraints of UAV dynamics and prior environmental information.Firstly,using the target probability distribution map,two strategies of information fusion and information diffusion are employed to solve the problem of environmental information inconsistency caused by different UAVs searching different areas,thereby improving the coordination of UAV groups.Secondly,the task region is decomposed into several high-value sub-regions by using data clustering method.Based on this,a hierarchical search strategy is proposed,which allows precise or rough search in different probability areas by adjusting the altitude of the aircraft,thereby improving the search efficiency.Third,the Elite Dung Beetle Optimization Algorithm(EDBOA)is proposed based on bionics by accurately simulating the social behavior of dung beetles to plan paths that satisfy the UAV dynamics constraints and adapt to the mountainous terrain,where the mountain is considered as an obstacle to be avoided.Finally,the objective function for path optimization is formulated by considering factors such as coverage within the task region,smoothness of the search path,and path length.The effectiveness and superiority of the proposed schemes are verified by the simulation.

A hierarchical multi-UAV cooperative framework for infrastructure inspection and reconstruction

Unmanned aerial vehicles(UAVs)are emerging as a powerful tool for inspections and repair works in large-scale and unstructured 3D infrastructures,but current approaches take a long time to cover the entire area.Planning using UAVs for inspections and repair works puts forward a requirement of improving time efficiency in large-scale and cluster environments.This paper presents a hierarchical multi-UAV cooperative framework for infrastructure inspection and reconstruction to balance the workload and reduce the overall task completion time.The proposed framework consists of two stages,the exploration stage and the exploitation stage,resolving the task in a sequential manner.At the exploration stage,the density map is developed to update global and local information for dynamic load-balanced area partition based on reconstructability and relative positions of UAVs,and the Voronoi-based planner is used to enable the UAVs to reach their best region.After obtaining the global map,viewpoints are generated and divided while taking into account the battery capacity of each UAV.Finally,a shortest path planning method is used to minimize the total traveling cost of these viewpoints for obtaining a high-quality reconstruction.Several experiments are conducted in both a simulated and real environment to show the time efficiency,robustness,and effectiveness of the proposed method.Furthermore,the whole system is implemented in real applications.

Multi-UAV Collaborative Edge Computing Algorithm for Joint Task Offloading and Channel Resource Allocation

Unmanned aerial vehicle (UAV)-based edge computing is an emerging technology that provides fast task processing for a wider area. To address the issues of limited computation resource of a single UAV and finite communication resource in multi-UAV networks, this paper joints consideration of task offloading and wireless channel allocation on a collaborative multi-UAV computing network, where a high altitude platform station (HAPS)is adopted as the relay device for communication between UAV clusters consisting of UAV cluster heads (ch-UAVs) and mission UAVs (m-UAVs). We propose an algorithm, jointing task offloading and wireless channel allocation to maximize the average service success rate (ASSR)of a period time. In particular,the simulated annealing(SA)algorithm with random perturbations is used for optimal channel allocation,aiming to reduce interference and minimize transmission delay.A multi-agent deep deterministic policy gradient (MADDPG) is proposed to get the best task offloading strategy. Simulation results demonstrate the effectiveness of the SA algorithm in channel allocation. Meanwhile,when jointly considering computation and channel resources,the proposed scheme effectively enhances the ASSR in comparison to other benchmark algorithms.

Distributed Flocking Algorithm for Multi-UAV System Based on Behavior Method and Topological Communication

There are many interesting flocking phenomena in nature,such as joint predation and group migration,and the intrinsic communication patterns of flocking are essential for studying group behavior.Traditional models of communication such as the pigeon flock model and the wolf pack model define all agents within a perceptual distance as the neighborhoods,and some models have fixed communicating numbers.There is a significant impact on the quality of the flocking formation when encountering poor initial state of the flocking,multiple obstacles,or loss of certain agents.To solve this problem,this paper proposes a local communication model with nearest agents in four directions.Based on this model and behavioral method,two distributed flocking formation algorithms are designed in this paper for different scenarios,namely the flocking algorithm and the circular formation algorithm.Numerical simulation results show that the flocking can pass through the obstacle area and re-formation smoothly,and also the formation quality of the flocking is better compared with the traditional communication model.

New multi-UAV formation keeping method based on improved artificial potential field

Formation keeping is important for multiple Unmanned Aerial Vehicles(multi-UAV)to fully play their roles in cooperative combats and improve their mission success rate.However,in practical applications,it is difficult to achieve formation keeping precisely and obstacle avoidance autonomously at the same time.This paper proposes a joint control method based on robust H∞ controller and improved Artificial Potential Field(APF)method.Firstly,we build a formation flight model based on the “Leader-Follower”structure and design a robust H∞ controller with three channels X,Y and Z to eliminate dynamic uncertainties,so as to realize high-precision formation keeping.Secondly,to fulfill obstacle avoidance efficiently in complex situations where UAVs fly at high speed with high inertia,this paper comes up with the improved APF method with deformation factor considered.The judgment criterion is proposed and applied to ensure flight safety.In the end,the simulation results show that the designed controller is effective with the formation keeping a high accuracy and in the meantime,it enables UAVs to avoid obstacles autonomously and recover the formation rapidly when coming close to obstacles.Therefore,the method proposed here boasts good engineering application prospect.

Hierarchical Decision-Making Framework for Multi-UAV Task Assignment via Enhanced Pigeon-Inspired Optimization

Effective task assignment decisions are paramount for ensuring reliable task execution in multi-UAV systems.However,in the development of feasible plans,challenges stemming from extensive and prolonged task requirements are encountered.This paper establishes a decision-making framework for multiple unmanned aerial vehicles(multi-UAV)based on the well-known pigeon-inspired optimization(PIO)algorithm.By addressing the problem from a hierarchical structural perspective,the initial stage involves minimizing the global objective of the flight distance cost after obtaining the entire task distribution and task requirements,utilizing the global optimization capability of the classical PIO algorithm to allocate feasible task spaces for each UAV.In the second stage,building upon the decisions made in the preceding stage,each UAV is abstracted as an agent maximizing its own task execution benefits.An improved version of the PIO algorithm modified with a sine-cosine search mechanism is proposed,enabling the acquisition of the optimal task execution sequence.Simulation experiments involving two different scales of UAVs validate the effectiveness of the proposed methodology.Moreover,dynamic events such as UAV damage and task changes are considered in the simulation to validate the efficacy of the two-stage framework.

基于改进鲸鱼算法的多无人机协同欺骗干扰技术

多机协同对组网雷达系统进行航迹欺骗干扰属于大规模优化问题,往往需要利用群体智能算法优化无人机的飞行任务,然而采用传统群体智能算法优化时往往会出现收敛速度慢、求解精度低等问题。针对这一问题,对标准鲸鱼优化算法进行了改进,提出了一种基于改进鲸鱼优化算法的多无人机协同欺骗干扰技术。首先建立了多无人机协同欺骗干扰组网雷达的数学模型以及对应的优化函数,然后在标准鲸鱼优化算法的基础上引入了自适应惯性权值,提高了算法的全局搜索能力和收敛速度。仿真实验表明,固定无人机数量为9架时,利用改进鲸鱼、标准鲸鱼、粒子群、蚁群4种算法分别优化多机协同欺骗干扰模型,得出改进鲸鱼优化算法平均运行时间最短,迭代次数最少,同时优化产生的实际航迹与理论值误差最小;逐步增加无人机数量至20架,利用上述四种算法进行模型求解时得出改进鲸鱼优化算法在不同无人机架数的条件下产生的假目标航迹条数均优于其他3种算法。