Two-layer formation-containment fault-tolerant control of fixed-wing UAV swarm for dynamic target tracking

This paper tackles the formation-containment control problem of fixed-wing unmanned aerial vehicle(UAV)swarm with model uncertainties for dynamic target tracking in three-dimensional space in the faulty case of UAVs’actuator and sensor.The fixed-wing UAV swarm under consideration is organized as a“multi-leader-multi-follower”structure,in which only several leaders can obtain the dynamic target information while others only receive the neighbors’information through the communication network.To simultaneously realize the formation,containment,and dynamic target tracking,a two-layer control framework is adopted to decouple the problem into two subproblems:reference trajectory generation and trajectory tracking.In the upper layer,a distributed finite-time estimator(DFTE)is proposed to generate each UAV’s reference trajectory in accordance with the control objective.Subsequently,a distributed composite robust fault-tolerant trajectory tracking controller is developed in the lower layer,where a novel adaptive extended super-twisting(AESTW)algorithm with a finite-time extended state observer(FTESO)is involved in solving the robust trajectory tracking control problem under model uncertainties,actuator,and sensor faults.The proposed controller simultaneously guarantees rapidness and enhances the system’s robustness with fewer chattering effects.Finally,corresponding simulations are carried out to demonstrate the effectiveness and competitiveness of the proposed two-layer fault-tolerant cooperative control scheme.

DQN-based decentralized multi-agent JSAP resource allocation for UAV swarm communication

It is essential to maximize capacity while satisfying the transmission time delay of unmanned aerial vehicle(UAV)swarm communication system.In order to address this challenge,a dynamic decentralized optimization mechanism is presented for the realization of joint spectrum and power(JSAP)resource allocation based on deep Q-learning networks(DQNs).Each UAV to UAV(U2U)link is regarded as an agent that is capable of identifying the optimal spectrum and power to communicate with one another.The convolutional neural network,target network,and experience replay are adopted while training.The findings of the simulation indicate that the proposed method has the potential to improve both communication capacity and probability of successful data transmission when compared with random centralized assignment and multichannel access methods.

Hybrid TDOA/FDOA and track optimization of UAV swarm based on A-optimality

The source location based on the hybrid time difference of arrival(TDOA)/frequency difference of arrival(FDOA) is a basic problem in wireless sensor networks, and the layout of sensors in the hybrid TDOA/FDOA positioning will greatly affect the accuracy of positioning. Using unmanned aerial vehicle(UAV) as base stations, by optimizing the trajectory of the UAV swarm, an optimal positioning configuration is formed to improve the accuracy of the target position and velocity estimation. In this paper, a hybrid TDOA/FDOA positioning model is first established, and the positioning accuracy of the hybrid TDOA/FDOA under different positioning configurations and different measurement errors is simulated by the geometric dilution of precision(GDOP) factor. Second, the Cramer-Rao lower bound(CRLB) matrix of hybrid TDOA/FDOA location under different moving states of the target is derived theoretically, the objective function of the track optimization is obtained, and the track of the UAV swarm is optimized in real time. The simulation results show that the track optimization effectively improves the accuracy of the target position and velocity estimation.

Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization

This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,length and angle variable rate.First,a three-dimensional(3D)modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs.Considering the length,height and tuning angle of a path,the path planning of R-UAVs is described as a tri-objective optimization problem.Then,an improved multi-objective particle swarm optimization algorithm is developed.To render the algorithm more effective in dealing with this problem,a vibration function is introduced into the collided solutions to improve the algorithm efficiency.Meanwhile,the selection of the global best position is taken into account by the reference point method.Finally,the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine.Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.

A cooperative detection game:UAV swarm vs.one fast intruder

This paper studies a special defense game using unmanned aerial vehicle(UAV)swarm against a fast intruder.The fast intruder applies an offensive strategy based on the artificial potential field method and Apollonius circle to scout a certain destination.As defenders,the UAVs are arranged into three layers:the forward layer,the midfield layer and the back layer.The co-defense mechanism,including the role derivation method of UAV swarm and a guidance law based on the co-defense front point,is introduced for UAV swarm to co-detect the intruder.Besides,five formations are designed for comparative analysis when ten UAVs are applied.Through Monte Carlo experiments and ablation experiment,the effectiveness of the proposed co-defense method has been verified.

Deep reinforcement learning for UAV swarm rendezvous behavior

The unmanned aerial vehicle(UAV)swarm technology is one of the research hotspots in recent years.With the continuous improvement of autonomous intelligence of UAV,the swarm technology of UAV will become one of the main trends of UAV development in the future.This paper studies the behavior decision-making process of UAV swarm rendezvous task based on the double deep Q network(DDQN)algorithm.We design a guided reward function to effectively solve the problem of algorithm convergence caused by the sparse return problem in deep reinforcement learning(DRL)for the long period task.We also propose the concept of temporary storage area,optimizing the memory playback unit of the traditional DDQN algorithm,improving the convergence speed of the algorithm,and speeding up the training process of the algorithm.Different from traditional task environment,this paper establishes a continuous state-space task environment model to improve the authentication process of UAV task environment.Based on the DDQN algorithm,the collaborative tasks of UAV swarm in different task scenarios are trained.The experimental results validate that the DDQN algorithm is efficient in terms of training UAV swarm to complete the given collaborative tasks while meeting the requirements of UAV swarm for centralization and autonomy,and improving the intelligence of UAV swarm collaborative task execution.The simulation results show that after training,the proposed UAV swarm can carry out the rendezvous task well,and the success rate of the mission reaches 90%.

Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior

Cooperative search-attack is an important application of unmanned aerial vehicle(UAV)swarm in military field.The coupling between path planning and task allocation,the heterogeneity of UAVs,and the dynamic nature of task environment greatly increase the complexity and difficulty of the UAV swarm cooperative search-attack mission planning problem.Inspired by the collaborative hunting behavior of wolf pack,a distributed selforganizing method for UAV swarm search-attack mission planning is proposed.First,to solve the multi-target search problem in unknown environments,a wolf scouting behavior-inspired cooperative search algorithm for UAV swarm is designed.Second,a distributed self-organizing task allocation algorithm for UAV swarm cooperative attacking of targets is proposed by analyzing the flexible labor division behavior of wolves.By abstracting the UAV as a simple artificial wolf agent,the flexible motion planning and group task coordinating for UAV swarm can be realized by self-organizing.The effectiveness of the proposed method is verified by a set of simulation experiments,the stability and scalability are evaluated,and the integrated solution for the coupled path planning and task allocation problems for the UAV swarm cooperative search-attack task can be well performed.

Cooperative Search of UAV Swarm Based on Ant Colony Optimization with Artificial Potential Field

An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed architecture where each UAV is considered as an ant and makes decision autonomously.At each decision step,the ants choose the next gird according to the state transition rule and update its own artificial potential field and pheromone map based on the current search results.Through iterations of this process,the cooperative search of UAV swarm for mission area is realized.The state transition rule is divided into two types.If the artificial potential force is larger than a threshold,the deterministic transition rule is adopted,otherwise a heuristic transition rule is used.The deterministic transition rule can ensure UAVs to avoid the threat or approach the target quickly.And the heuristics transition rule considering the pheromone and heuristic information ensures the continuous search of area with the goal of covering more unknown area and finding more targets.Finally,simulations are carried out to verify the effectiveness of the proposed ACOAPF algorithm for cooperative search mission of UAV swarm.

Distributed tracking control of unmanned aerial vehicles under wind disturbance and model uncertainty

A distributed robust method is developed for cooperative tracking control of unmanned aerial vehicles under unknown wind disturbance and model uncertainty. The communication network among vehicles is a directed graph with switching topology. Each vehicle can only share its states with its neighbors. Dynamics of the vehicles are nonlinear and affected by the wind disturbance and model uncertainty. Feedback linearization is adopted to transform the dynamics of vehicles into linear systems. To account for the wind disturbance and model uncertainty, a robust controller is designed for each vehicle such that all vehicles ultimately synchronize to the virtual leader in the three-dimensional path. It is theoretically shown that the position states of the vehicles will converge to that of the virtual leader if the communication network has a directed spanning tree rooted at the virtual leader. Furthermore, the robust controller is extended to address the formation control problem. Simulation examples are also given to illustrate the effectiveness of the proposed method. © 2016 Beijing Institute of Aerospace Information.

Decentralized Multiagent Task Planning for Heterogeneous UAV Swarm

A decentralized task planning algorithm is proposed for heterogeneous unmanned aerial vehicle(UAV)swarm with different capabilities.The algorithm extends the consensus-based bundle algorithm(CBBA)to account for a more realistic and complex environment.The extension of the algorithm includes handling multi-agent task that requires multiple UAVs collaboratively completed in coordination,and consideration of avoiding obstacles in task scenarios.We propose a new consensus algorithm to solve the multi-agent task allocation problem and use the Dubins algorithm to design feasible paths for UAVs to avoid obstacles and consider motion constraints.Experimental results show that the CBBA extension algorithm can converge to a conflict-free and feasible solution for multi-agent task planning problems.

TDOA and track optimization of UAV swarm based on D-optimality

To solve the problem of time difference of arrival(TDOA)positioning and tracking of targets by the unmanned aerial vehicles(UAV)swarm in future air combat,this paper adopts the TDOA positioning method and uses time difference sensors of the UAV swarm to locate target radiation sources.Firstly,a TDOA model for the target is set up for the UAV swarm under the condition that the error variance varies with the received signal-to-noise ratio.The accuracy of the positioning error is analyzed by geometric dilution of precision(GDOP).The D-optimality criterion of the positioning model is theoretically derived.The target is positioned and settled,and the maximum value of the Fisher information matrix determinant is used as the optimization objective function to optimize the track of the UAV in real time.Simulation results show that the track optimization improves the positioning accuracy and stability of the UAV swarm to the target.

Bibliometric analysis of UAV swarms

Projects on unmanned aerial vehicle(UAV) swarms have been initiated in a big way in the last few years, especially from 2015 to 2016. As a result, the number of related works on UAV swarms has been on the rise, with the rate of growth dramatically accelerating since 2017. This research conducts a bibliometric analysis of robotics swarms and UAV swarms to answer the following questions:(i) Disciplines mentioned in the UAV swarms research.(ii) The future development trends and hotspots in the UAV swarms research.(iii) Tracking related outcomes in the UAV swarms research.

A UAV collaborative defense scheme driven by DDPG algorithm

The deep deterministic policy gradient(DDPG)algo-rithm is an off-policy method that combines two mainstream reinforcement learning methods based on value iteration and policy iteration.Using the DDPG algorithm,agents can explore and summarize the environment to achieve autonomous deci-sions in the continuous state space and action space.In this paper,a cooperative defense with DDPG via swarms of unmanned aerial vehicle(UAV)is developed and validated,which has shown promising practical value in the effect of defending.We solve the sparse rewards problem of reinforcement learning pair in a long-term task by building the reward function of UAV swarms and optimizing the learning process of artificial neural network based on the DDPG algorithm to reduce the vibration in the learning process.The experimental results show that the DDPG algorithm can guide the UAVs swarm to perform the defense task efficiently,meeting the requirements of a UAV swarm for non-centralization,autonomy,and promoting the intelligent development of UAVs swarm as well as the decision-making process.

用于UAV运动目标搜索的自适应粒子群算法

针对运动编码粒子群算法在处理无人机运动目标搜索问题时存在被其他高概率区域混淆、算法搜索成功率不够高的问题,提出了一种基于运动编码的自适应学习策略粒子群优化算法以优化无人机飞行路径。该算法先设计了适应于各种搜索场景的初始化方案;再融入聚类算法用以动态划分粒子群,并改进了子群中不同类型粒子的更新方程以适应路径规划中的粒子子群;最后添加了自适应学习策略以控制参数,旨在保持收敛速度的基础上提高搜索到最优路径的概率。在不同搜索场景下的实验结果表明,与运动编码粒子群优化算法相比,算法的检测性能提升了6%。此外,与其他元启发式优化算法的对比结果也展示了算法的优势。

航母舰载无人机全自动着舰技术特点分析

全自动着舰技术是无人机上舰必须解决的“使能”技术。2013年,美军已完成X-47B无人机验证机在航母上的全自动着舰试验验证,为其发展MQ-25A“黄貂鱼”航母舰载无人机装备奠定了良好的技术基础。在梳理飞机着陆与着舰差异的基础上,给出航母舰载无人机全自动着舰的通用流程;从地位作用、工作范围、系统构架、系统要求、实现手段以及关键技术支撑等角度,对比有人机重点分析了航母舰载无人机全自动着舰的特点,为后续关键技术研究和武器装备建设发展提供参考。

人工等离子体云团与无人机群的散射研究

电离层中释放的金属蒸气产生人工等离子体云团,其可显著改变无线电波传播。本文利用几何绕射理论(geometrical theory of diffraction, GTD)和有限元法(finite element method, FEM)相结合的方法,给出了经由天线、人工等离子云团和无人机(unmanned aerial vehicle, UAV)群组成的传播链路中信号强度计算方法。利用30~70 MHz甚高频(very high frequency, VHF)信号研究人工等离子体云团与UAV群的复合散射特性,得出如下结论:接收功率随着信号频率增加呈下降趋势;当机群由N架UAV构成时,阵因子迭加使机群雷达散射截面(radar cross section, RCS)出现一定的起伏,同相迭加时,接收功率可比单个UAV高约20lg N dB;利用人工等离子体云团散射可实现VHF频段用于对米级尺度RCS目标进行超视距探测,有助于解决紧急情况下电离层扰动对高频探测的不利影响。

无人机17kW电机振动噪声分析与巡航转速下尖端噪声优化

随着无人机的迅速发展,噪声问题影响消费者体验及AI交互、语音识别等技术,限制了无人机应用潜力。该文针对一台17 kW无人机用外转子永磁同步电机进行研究。为降低电机尖端振动噪声,且保留原电机电磁性能,重点提出优化磁极和定子开槽的方法。具体以平均转矩、转矩脉动等作为约束条件,构建多目标优化数学模型,并利用混合粒子群优化算法求解。该文深入探讨磁极参数、定子开槽对低阶次径向气隙磁通密度空间谐波特征的影响。并对电机转子模态仿真,以研究径向电磁力与空间模态的作用机理。在多转速情况下,以巡航转速为重点,分析整体电机电磁振动噪声特征。最后,仿真和实验结果表明,电机在巡航转速下的尖端噪声显著减小。验证了优化结构对无人机电机尖端振动噪声有明显抑制作用,对解决无人机噪声问题具有重要意义。

陆上机动力量反无人机集群作战的挑战与对策

面对无人机集群日益凸显的作战威胁,反无人机集群技术研究被提上日程。针对陆上机动力量作战特点,从战术层面和战略层面对反无人机集群作战问题进行了研究。首先,分析了反无人机集群作战的特点;然后,从侦察预警、拦截打击和干扰压制3个方面,提出了陆上机动力量反无人机集群的战法运用方式;最后,给出了陆上机动力量反无人机集群作战能力建设的意见和建议。

无人机集群任务分配技术研究综述

任务分配是无人机集群实现高效遂行作战任务的关键技术。随着无人机集群技术的发展和作战样式的转变,无人机集群的作战任务领域不断拓展,任务分配所涵盖的范围不断扩大,任务分配问题的规模和复杂性不断增加,这都对无人机集群任务分配技术提出了新的挑战。本文对无人机集群作战理论、任务分配建模、任务预重分配算法、异构无人系统联合应用下任务分配的研究现状进行了全面的总结,凝练了目前无人机集群任务分配技术面临的通用化建模、面向多任务的任务预分配算法最优解求解、有限时间下面向突发事件的任务重分配算法寻优、路径规划紧耦合下面向大规模异构无人系统的协同任务分配等问题,并针对性地论述了未来无人机集群任务分配技术的若干发展方向,为提升无人机集群任务分配的求解质量和求解速度提供新的研究思路和解决途径,对于全面了解无人机集群任务分配技术具有重要参考意义。

UAV集群自组织飞行建模与控制策略研究

针对多无人航空器协同控制难题,聚焦无人航空器集群(unmanned aircraft vehicles swarm,UAVS)自组织飞行建模与控制展开研究。基于集群智能理论建立了UAVS系统概念模型,在考虑个体排斥作用、一致作用、吸引作用和个体行动意愿作用4种因素的情况下建立了集群运动的变系数(repulsion-matching-attracting-desire,RMAD)控制器模型,以此为基础,研究了所有个体掌握航迹信息和部分个体掌握航迹信息两种情况下UAVS自组织飞行控制问题,提出UAVS自组织飞行控制策略,实现了UAVS可控性自组织飞行。仿真实验结果表明构造的UAVS运动的RMAD模型及控制方法是可行的,为UAVS的工程应用奠定理论和实验基础。