Multi-Robot Collaborative Hunting in Cluttered Environments With Obstacle-Avoiding Voronoi Cells

This work proposes an online collaborative hunting strategy for multi-robot systems based on obstacle-avoiding Voronoi cells in a complex dynamic environment. This involves firstly designing the construction method using a support vector machine(SVM) based on the definition of buffered Voronoi cells(BVCs). Based on the safe collision-free region of the robots, the boundary weights between the robots and the obstacles are dynamically updated such that the robots are tangent to the buffered Voronoi safety areas without intersecting with the obstacles. Then, the robots are controlled to move within their own buffered Voronoi safety area to achieve collision-avoidance with other robots and obstacles. The next step involves proposing a hunting method that optimizes collaboration between the pursuers and evaders. Some hunting points are generated and distributed evenly around a circle. Next, the pursuers are assigned to match the optimal points based on the Hungarian algorithm.Then, a hunting controller is designed to improve the containment capability and minimize containment time based on collision risk. Finally, simulation results have demonstrated that the proposed cooperative hunting method is more competitive in terms of time and travel distance.

Integration of bio-inspired limb-like structure damping into motor suspension of high-speed trains to enhance bogie hunting stability

Hunting stability is an important performance criterion in railway vehicles.This study proposes an incorporation of a bio-inspired limb-like structure(LLS)-based nonlinear damping into the motor suspension system for traction units to improve the nonlinear critical speed and hunting stability of high-speed trains(HSTs).Initially,a vibration transmission analysis is conducted on a HST vehicle and a metro vehicle that suffered from hunting motion to explore the effect of different motor suspension systems from on-track tests.Subsequently,a simplified lateral dynamics model of an HST bogie is established to investigate the influence of the motor suspension on the bogie hunting behavior.The bifurcation analysis is applied to optimize the motor suspension parameters for high critical speed.Then,the nonlinear damping of the bio-inspired LLS,which has a positive correlation with the relative displacement,can further improve the modal damping of hunting motion and nonlinear critical speed compared with the linear motor suspension system.Furthermore,a comprehensive numerical model of a high-speed train,considering all nonlinearities,is established to investigate the influence of different types of motor suspension.The simulation results are well consistent with the theoretical analysis.The benefits of employing nonlinear damping of the bio-inspired LLS into the motor suspension of HSTs to enhance bogie hunting stability are thoroughly validated.

动脉瘤性蛛网膜下腔出血患者Hunt-Hess分级与并发症及功能障碍相关性分析

目的:探讨动脉瘤性蛛网膜下腔出血(aneurysmal subarachnoid hemorrhage,aSAH)患者的Hunt-Hess分级与并发症及神经系统功能障碍的相关性。方法:回顾性分析2011年10月到2021年10月收治的195例aSAH患者的临床资料,将Hunt-HessⅠ~Ⅲ级纳入低级别组(86例),Ⅳ~Ⅴ级纳入高级别组(109例)。比较2组患者的一般情况、量表评分、并发症及主要功能障碍,并将差异有统计学意义的指标进行多因素Logistic回归分析。结果:Hunt-Hess分级与脑积水(OR 2.850、95%CI 1.560~5.206),运动功能障碍(OR 3.522、95%CI 2.026~6.121),语言功能障碍(OR 1.928、95%CI 1.062~3.500),意识障碍(OR 3.662、95%CI 1.558~8.607),吞咽障碍(OR 2.383、95%CI 1.248~4.550)呈正相关;与年龄(OR 0.970、95%CI 0.945~0.996),继发脑梗死(OR 0.414、95%CI 0.227~0.754),Fugl-Meyer平衡评分(OR 0.890、95%CI 0.818~0.968),ADL评分(OR 0.983、95%CI 0.971~0.994)呈负相关。结论:Hunt-Hess高级别组的患者脑积水的发生比率更高,整体功能障碍更重,年轻患者的存活率更高;但Hunt-Hess级别高低,患者均有认知障碍的风险,应注意及时筛查并进行个体化康复。

Community Perceptions and Socio-Demographic Influence on Trophy Hunting in Rungwa Game Reserve, Tanzania

The trophy hunting industry in Tanzania plays a significant role in wildlife conservation as well as economic and community development. Trophy hunting has been conducted in Rungwa Game Reserve (RGR) for several decades;however, the trophy hunting opinions from the local communities living adjacent to the reserve are not well documented. This study aimed to assess the awareness and attitudes of local communities living adjacent to Rungwa Game Reserves toward trophy hunting. Furthermore, the study assessed factors that influenced the opinions of participants by using structural equation modeling. We used semi-structured interviews and key informant interviews in the three villages adjacent to RGR. The local communities are aware of existing hunting operations around the game reserves. The study found positive attitudes among local communities toward trophy hunting, only if local people accrue benefits from hunting operations. The community’s attitudes towards trophy hunting varied across household size and occupation. Education and household position had an indirect influence on the community’s attitude. Our findings suggest that socio-demographic variables are important to consider when assessing awareness and attitudes toward trophy hunting. Local communities are willing to support trophy hunting operations if the benefits obtained from hunting are significant and it can improve wildlife conservation and their livelihood. In conclusion, trophy hunting is important to local communities living adjacent to protected areas, and banning them may have a significant impact on their livelihood and wildlife conservation. Therefore, it is recommended that conservation policies and interventions consider the dual significance of trophy hunting, fostering strategies that balance socio-economic benefits for communities and wildlife conservation.

Accurate threat hunting in industrial internet of things edge devices

Industrial Internet of Things(IIoT)systems depend on a growing number of edge devices such as sensors,controllers,and robots for data collection,transmission,storage,and processing.Any kind of malicious or abnormal function by each of these devices can jeopardize the security of the entire IIoT.Moreover,they can allow malicious software installed on end nodes to penetrate the network.This paper presents a parallel ensemble model for threat hunting based on anomalies in the behavior of IIoT edge devices.The proposed model is flexible enough to use several state-of-the-art classifiers as the basic learner and efficiently classifies multi-class anomalies using the Multi-class AdaBoost and majority voting.Experimental evaluations using a dataset consisting of multi-source normal records and multi-class anomalies demonstrate that our model outperforms existing approaches in terms of accuracy,F1 score,recall,and precision.

Research on hunting stability and bifurcation characteristics of nonlinear stochastic wheelset system

A stochastic wheelset model with a nonlinear wheel-rail contact relationship is established to investigate the stochastic stability and stochastic bifurcation of the wheelset system with the consideration of the stochastic parametric excitations of equivalent conicity and suspension stiffness.The wheelset is systematized into a onedimensional(1D)diffusion process by using the stochastic average method,the behavior of the singular boundary is analyzed to determine the hunting stability condition of the wheelset system,and the critical speed of stochastic bifurcation is obtained.The stationary probability density and joint probability density are derived theoretically.Based on the topological structure change of the probability density function,the stochastic Hopf bifurcation form and bifurcation condition of the wheelset system are determined.The effects of stochastic factors on the hunting stability and bifurcation characteristics are analyzed,and the simulation results verify the correctness of the theoretical analysis.The results reveal that the boundary behavior of the diffusion process determines the hunting stability of the stochastic wheelset system,and the left boundary characteristic value cL=1 is the critical state of hunting stability.Besides,stochastic D-bifurcation and P-bifurcation will appear in the wheelset system,and the critical speeds of the two kinds of stochastic bifurcation decrease with the increase in the stochastic parametric excitation intensity.

Flight Behavioural Responses for African Ungulates across Species and Vegetation Covers in a Trophy Hunting Ecosystem: A Case Study from Selous Game Reserve, Tanzania

Trophy hunting has severe consequences on wild animals’ behaviors, which in return has implications for affecting wildlife populations. The Selous Game Reserve is a protected area in Tanzania that has been subjected to commercial trophy hunting for decades, and information about the effects of trophy hunting on animals’ welfare is still scarce. The Flight Initiating Distance (FID) can be a good measure to evaluate the welfare of animals and the level of risk perception towards anthropogenic disturbances, including trophy hunting. The study used linear mixed models to assess the flight responses of twelve commonly hunted species in the Selous game reserve (S.G.R.). The study compared animal vigilance between species, vegetation types, and group size. The FID varied between species, with which more vigilance was observed in zebras, elands, wildebeests, and sable antelopes. The study found a significant influence of vegetation cover on individual species’ FID. Further, the study found a significant influence of group size on animals’ vigilance (L. M. M., 95% CI = 0.590 - 4.762), in which there was a decrease in FID with an increase in group size for wildebeests. At the same time, other species, such as buffaloes, eland, hartebeests, and zebras, had their FIDs increasing with the increase in group size. We conclude that the impact of trophy hunting on savannah ungulates varies between species, vegetation covers, and group size of individual species. Regulatory authorities should consider minimum approach distances by trophy hunters in different vegetation cover to reduce animal biological disturbances.

Arabic Sign Language Gesture Classification Using Deer Hunting Optimization with Machine Learning Model

Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities.Several models have been available in the literature for sign language detection and classification for enhanced outcomes.But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks.This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)model.The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures.The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)model.For gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language gestures.Lastly,the DHO algorithm is utilized for parameter optimization of the MLP model.The experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct aspects.The comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.

Economic Valuation of Auctioned Tourist Hunting Blocks in Tanzania

Economic valuation of ecosystems is increasingly being recognized as an important exercise to inform sustainable utilization and conservation of natural assets. It helps in planning and establishing fair profit margins that accrue either directly or indirectly from the consumptive and non-consumptive uses of ecosystem goods and services. This paper is based on a study which estimated the economic values of tourist hunting blocks (HBs) in Tanzania using the Analytic Multicriteria Valuation Method (AMUVAM). The study used a sample size of 12 out of 24 vacant hunting blocks which were to be auctioned to potential hunting companies in December 2022. The economic values of HBs were estimated using the time horizon of 10 years (the mean tenure for winning company). The results show that the economic values ranged from USD 6,215,588 to USD 653,470,695 per hunting block and the Existence Value (EV) constituted about 19% of the Total Economic Value (TEV). EV ranged from USD 632,210 to USD 125,147,285. The study underscores the need for decisions to allocate ecosystems, such as HBs, to both direct and indirect uses, to be guided by a though understanding of their values. We further recommend building the capacity of staff charged with the role of managing and allocating uses of these ecosystems to enable them undertake economic valuation of ecosystems using both simple and more robust analytical tools, such as the GIS, relational databases, and worldwide websites based tools, like InVEST (Integrated Valuation of Environmental Services and Tradeoffs), ARIES (Artificial Intelligence for Ecosystem Services), and Co$ting Nature.

Cooperative multi-target hunting by unmanned surface vehicles based on multi-agent reinforcement learning

To solve the problem of multi-target hunting by an unmanned surface vehicle(USV)fleet,a hunting algorithm based on multi-agent reinforcement learning is proposed.Firstly,the hunting environment and kinematic model without boundary constraints are built,and the criteria for successful target capture are given.Then,the cooperative hunting problem of a USV fleet is modeled as a decentralized partially observable Markov decision process(Dec-POMDP),and a distributed partially observable multitarget hunting Proximal Policy Optimization(DPOMH-PPO)algorithm applicable to USVs is proposed.In addition,an observation model,a reward function and the action space applicable to multi-target hunting tasks are designed.To deal with the dynamic change of observational feature dimension input by partially observable systems,a feature embedding block is proposed.By combining the two feature compression methods of column-wise max pooling(CMP)and column-wise average-pooling(CAP),observational feature encoding is established.Finally,the centralized training and decentralized execution framework is adopted to complete the training of hunting strategy.Each USV in the fleet shares the same policy and perform actions independently.Simulation experiments have verified the effectiveness of the DPOMH-PPO algorithm in the test scenarios with different numbers of USVs.Moreover,the advantages of the proposed model are comprehensively analyzed from the aspects of algorithm performance,migration effect in task scenarios and self-organization capability after being damaged,the potential deployment and application of DPOMH-PPO in the real environment is verified.

Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data

Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and emotions.Detection of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so on.Emotion is expressed in several means,like facial and speech expressions,gestures,and written text.Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)domains.This article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English Twitter Data in this study.The presented DHODBN-EC model aims to examine the existence of distinct emotion classes in tweets.At the introductory level,the DHODBN-EC technique pre-processes the tweets at different levels.Besides,the word2vec feature extraction process is applied to generate the word embedding process.For emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class labels.Lastly,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique.An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.

Can rifaximin for hepatic encephalopathy be discontinued during broad-spectrum antibiotic treatment?

Hepatic encephalopathy(HE)is a formidable complication in patients with decompensated cirrhosis,often necessitating the administration of rifaximin(RFX)for effective management.RFX,is a gut-restricted,poorly-absorbable oral rifamycin derived antibiotic that can be used in addition to lactulose for the secondary prophylaxis of HE.It has shown notable reductions in infection,hospital readmission,duration of hospital stay,and mortality.However,limited data exist about the concurrent use of RFX with broad-spectrum antibiotics,because the patients are typically excluded from studies assessing RFX efficacy in HE.A pharmacist-driven quasi-experimental pilot study was done to address this gap.They argue against the necessity of RFX in HE during broad-spectrum antibiotic treatment,particularly in critically ill patients in intensive care unit(ICU).The potential for safe RFX discontinuation without adverse effects is clearly illuminated and valuable insight into the optimization of therapeutic strategies is offered.The findings also indicate that RFX discontinuation during broadspectrum antibiotic therapy was not associated with higher rates of delirium or coma,and this result remained robust after adjustment in multivariate analysis.Furthermore,rates of other secondary clinical and safety outcomes,including ICU mortality and 48-hour changes in vasopressor requirements,were comparable.However,since the activity of RFX is mainly confined to the modulation of gut microbiota,its potential utility in patients undergoing extensive systemic antibiotic therapy is debatable,given the overlapping antibiotic activity.Further,this suggests that the action of RFX on HE is class-specific(related to its activity on gut microbiota),rather than drug-specific.A recent double-blind randomized controlled(ARiE)trial provided further evidence-based support for RFX withdrawal in critically ill cirrhotic ICU patients receiving broad-spectrum antibiotics.Both studies prompt further discussion about optimal therapeutic strategy for patients facing the dual challenge of HE and systemic infections.Despite these compelling results,both studies have limitations.A prospective,multi-center evaluation of a larger sample,with placebo control,and comprehensive neurologic evaluation of HE is warranted.It should include an exploration of longer-term outcome and the impact of this protocol in non-critically ill liver disease patients.

甘蓝型油菜全基因组cDNA FOX-hunting过表达文库构建

为系统解析甘蓝型油菜基因功能,结合FOX-hunting和Gateway重组技术,以4种激素和4种非生物胁迫处理的甘蓝型油菜幼苗根和叶片及成熟期种子和角果全长cDNA为混样,构建了全长cDNA FOX-hunting过表达文库。经检测,初级文库滴度为3.0×10^(6),库容为1.2×10^(7)CFU;次级FOX-hunting过表达文库滴度为2.6×10^(6),库容为1.04×10^(7)CFU。次级文库菌液PCR鉴定表明,阳性克隆率为96%,扩增均为250~3000 bp随机分布的单一条带,表明初级文库已较好地重组到pJVC55载体上。农杆菌转化后的克隆测序发现,140个克隆中有12个克隆为非特异扩增。GO富集分析发现剩余128个基因中,除21个基因功能富集于光合作用途径外,其余基因在转录因子和激酶中随机分布,且功能涉及器官发育、物质代谢和胁迫响应等多个方面。上述结果表明,本研究构建的甘蓝型油菜FOX-hunting过表达文库无功能偏向性、多态性丰富、完整性高,可作为油菜功能基因组的研究基础。

Cool Hunting研究在产品创新设计中的应用

在市场竞争白热化的当下,用户体验日趋重要,如何让产品/服务更具前瞻性和创新性,更好地满足用户需求,我们引入了Cool Hunting研究,结合家电领域的特点,进行酷故事的收集与整理,并通过分析与酷创新,发现家电领域的产品/服务的创新机会,以期更好地满足市场和用户需求。

三维精准手术计划对减少高Hunt-Hess分级动脉瘤夹闭术中并发症的意义

目的探讨术者在进行合并严重颅内血肿的前循环动脉瘤夹闭时,能否在三维个体化的精准手术计划指导下,减少术中并发症的发生。方法使用3D Slicer软件对23例高Hunt-Hess分级的前循环动脉瘤患者术前CT和CTA图像进行融合及三维体绘制重建,制定精确的个体化手术方案并指导动脉瘤夹闭术,并比较规划手术中与实际术中发现的一致性。结果按照精准手术方案进行头位摆放和开颅,能明显减少术中对患者头位的调整及因术野狭窄而二次磨除蝶骨嵴;规划术野中显示的瘤颈形态、瘤颈瘤体过渡区薄弱部位以及瘤颈旁细小动脉对实际术中发现判断的灵敏度分别为90%、50%和75%,特异度分别为100%、100%和93.3%,Kappa值分别为0.701、0.623和0.732;规划夹闭方式对实际夹闭方式判断的灵敏度为100%,特异度25%,Kappa系数0.355。结论三维个体化的精准手术计划,能有效帮助术者对合并大量颅内出血的前循环动脉瘤手术夹闭过程做出精确预判,有利于减少术中并发症。

FOX Hunting System——一种新的功能基因筛选技术

FOX hunting system是近几年发展起来的又一新型分子生物学研究工具。在2006年,FOX hunting system作为一种植物功能获得性基因筛选技术首次被提出,可用于系统性阐明植物基因的功能。FOX hunting system可以在转基因植株中过量表达单个或有限数量的全长cDNA,这些异常表达的全长cDNA可能会导致显著的突变表型。根据研究目的对表型等方面发生改变的功能获得性突变植株进行筛选和解析,可以推断出转基因突变植株中所携带外源基因的功能。本文将就FOX hunting system的开发、流程、应用和发展等方面内容进行详细的阐述。

Influence of Vehicle Parameters on Critical Hunting Speed Based on Ruzicka Model

While introducing foreign advanced technology and cooperating with Chinese famous research institutes,the high-speed vehicles are designed and take the major task of passenger transport in China.In high-speed vehicle,the characteristic of shock absorber is an important parameter which determines overall behavior of the vehicle.The most existing researches neglect the influence of the series stiffness of the shock absorber on the vehicle dynamic behavior and have one-sided views on the equivalent conicity of wheel tread.In this paper,a high speed passenger vehicle in China is modeled to investigate the effect of the parameters taking series hydraulic shock absorber stiffness into consideration on Ruzicka model.Using the vehicle dynamic model,the effect of main suspension parameters on critical speed is studied.In order to verify the reasonableness of shock absorber parameter settings,vibration isolation characteristics are calculated and the relationship between suspension parameters and the vehicle critical hunting speed is studied.To study the influence of equivalent conicity on vehicle dynamic behavior,a series of wheel treads with different conicities are set and the vehicle critical hunting speeds with different wheel treads are calculated.The discipline between the equivalent conicity of wheel tread and critical speed are obtained in vehicle nonlinear system.The research results show that the critical speed of vehicle much depends on wheelset positioning stiffness and anti-hunting motion damper,and the series stiffness produces notable effect on the vehicle dynamic behavior.The critical speed has a peak value with the equivalent conicity increasing,which is different from the traditional opinion in which the critical speed will decrease with the conicity increasing.The relationship between critical speed and conicity of wheel tread is effected by the suspension parameters of the vehicle.The study results obtained offer a method and useful data to designing the parameters of the high speed vehicle and simulation study.

Influences of aerodynamic loads on hunting stability of high-speed railway vehicles and parameter studies

The influences of steady aerodynamic loads on hunting stability of high-speed railway vehicles were investigated in this study.A mechanism is suggested to explain the change of hunting behavior due to actions of aerodynamic loads：the aerodynamic loads can change the position of vehicle system（consequently the contact relations）,the wheel/rail normal contact forces,the gravitational restoring forces/moments and the creep forces/moments.A mathematical model for hunting stability incorporating such influences was developed.A computer program capable of incorporating the effects of aerodynamic loads based on the model was written,and the critical speeds were calculated using this program.The dependences of linear and nonlinear critical speeds on suspension parameters considering aerodynamic loads were analyzed by using the orthogonal test method,the results were also compared with the situations without aerodynamic loads.It is shown that the most dominant factors a ff ecting linear and nonlinear critical speeds are different whether the aerodynamic loads considered or not.The damping of yaw damper is the most dominant influencing factor for linear critical speeds,while the damping of lateral damper is most dominant for nonlinear ones.When the influences of aerodynamic loads are considered,the linear critical speeds decrease with the rise of cross wind velocity,whereas it is not the case for the nonlinear critical speeds.The variation trends of critical speeds with suspension parameters can be significantly changed by aerodynamic loads.Combined actions of aerodynamic loads and suspension parameters also a ff ect the critical speeds.The effects of such joint action are more obvious for nonlinear critical speeds.

An Exploration of the Relation between Hunting and Aggressiveness: Using Inmates Networks at Prison Secondary School as an Illustration

Aim of this research is to explore the relation of hunting with aggressiveness. For this purpose, two samples of prison inmates (who were students of the prison secondary school) were collected (class A = 23, class B = 12). The network indicators for superficial, idiosyncratic and strategic behavior (particularly, aggressiveness), applied in Bekiari and Hasanagas (2016), which were based on the primary network variables (outdegree, indegree, Katz status, pagerank, authority) were used. Non-network variables were also used for the criminal profile of the inmates. The data were sampled with questionnaire. Spearman test was conducted for detecting correlation between aggressiveness and hunting and Principal Component Analysis was used for formulating a typology. The following results were produced: The criminal profile seems hardly to be related with hunting experience or attitude. The relation of hunting experience and attitude with the superficial, idiosyncratic and strategic aggressiveness was examined. These three occasions of aggressiveness are expected to describe the incidental reaction, impulsive and adaptive reaction, respectively. The hunting is quite irrelevant to the incidental reaction. The impulsiveness seems to be more strongly correlated with hunting. In case of adaptiveness, there are many similarities with impulsiveness. The typology which is based on the existence (or not) of hunting experience seems to be more insightful, as it reveals a gradual involvement of aggressiveness dimensions, from incidentality to impulsiveness and further to adaptiveness. The following behavioral patterns of gradually enhanced aggressiveness appear: incidentally just provoking, impulsively being aggressive but without harming, and adaptively being aggressive and harming.

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.