UAVs cooperative task assignment and trajectory optimization with safety and time constraints

This paper proposes new methods and strategies for Multi-UAVs cooperative attacks with safety and time constraints in a complex environment.Delaunay triangle is designed to construct a map of the complex flight environment for aerial vehicles.Delaunay-Map,Safe Flight Corridor(SFC),and Relative Safe Flight Corridor(RSFC)are applied to ensure each UAV flight trajectory's safety.By using such techniques,it is possible to avoid the collision with obstacles and collision between UAVs.Bezier-curve is further developed to ensure that multi-UAVs can simultaneously reach the target at the specified time,and the trajectory is within the flight corridor.The trajectory tracking controller is also designed based on model predictive control to track the planned trajectory accurately.The simulation and experiment results are presented to verifying developed strategies of Multi-UAV cooperative attacks.

Complex task planning method of space-aeronautics cooperative observation based on multi-layer interaction

With the new development trend of multi-resource coordinated Earth observation and the new goal of Earth observation application of“short response time,high observation accuracy,and wide coverage”,space-aeronautics cooperative complex task planning problem has become an urgent problem to be solved.The focus of this problem is to use multiple resources to perform collaborative observations on complex tasks.By analyzing the process from task assignment to receiving task observation results,we propose a multi-layer interactive task planning framework which is composed of a preprocessing method for complex tasks,a task allocation layer,a task planning layer,and a task coordination layer.According to the characteristics of the framework,a hybrid genetic parallel tabu(HGPT)algorithm is proposed on this basis.The algorithm uses genetic annealing algorithm(GAA),parallel tabu(PT)algorithm,and heuristic rules to achieve task allocation,task planning,and task coordination.At the same time,coding improvements,operator design,annealing operations,and parallel calculations are added to the algorithm.In order to verify the effectiveness of the algorithm,simulation experiments under complex task scenarios of different scales are carried out.Experimental results show that this method can effectively solve the problems of observing complex tasks.Meanwhile,the optimization effect and convergence speed of the HGPT is better than that of the related algorithms.

Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm

The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.

Optimization Task Scheduling Using Cooperation Search Algorithm for Heterogeneous Cloud Computing Systems

Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the web.As a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud computing.The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions.Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system.The diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing system.As a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the makespan.This research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing problem.The basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal solution.We assess our algorithm’s performance by running it through three scenarios with varying numbers of tasks.The findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.

Task assignment under constraint of timing sequential for cooperative air combat

According to the previous achievement, the task assignment under the constraint of timing continuity for a cooperative air combat is studied. An extensive task assignment scenario with the background of the cooperative air combat is proposed. The utility and time of executing a task as well as the continuous combat ability are defined. The concept of the matching method of weapon and target is modified based on the analysis of the air combat scenario. The constraint framework is also redefined according to a new objective function. The constraints of timing and continuity are formulated with a new method, at the same time, the task assignment and integer programming models of the cooperative combat are established. Finally, the assignment problem is solved using the integrated linear programming software and the simulation shows that it is feasible to apply this modified model in the cooperative air combat for tasks cooperation and it is also efficient to optimize the resource assignment.

A cooperative model of photovoltaic and electricity-to-hydrogen including green certificate trading under the conditional value at risk

Cooperation in energy systems is no longer limited to the distribution of electricity,and more attention is paid to the trading of green certificates(GCs).This paper proposed a cooperative method for photovoltaic(PV)and electric-to-hydrogen(EH)trading,including GC trading under risk management.First,a novel PV and EH model is established and the cooperation mechanism is analyzed.Meanwhile,PV and EH models were risk-controlled using the conditional value at risk to reduce the impact of the uncertainty of PV electricity and EH loads.Then,the PV-EH cooperative model was established based on cooperative game theory;this was then divided into two subproblems of“cooperative benefit maximization”and“transaction payment negotiation,”and the above two subproblems were solved distributively by alternating direction multiplier method(ADMM).Only energy transactions and price negotiations were conducted between the PV and EH,which can protect the privacy and confidentiality of each entity.Finally,the effectiveness of the cooperation model was verified using a practical engineering case.The simulation results show that the cooperation of the PV-EH can significantly improve the operational efficiency of each entity and the overall efficiency of the cooperation and realize the efficient redistribution of electricity and GC.

Promotion of interaction in cooperative learning task

How to promote interaction in cooperative learning tasks is discussed from a theoretical perspective in order to maximize the benefits of cooperative learning. A classroom instructional model is presented and examined to illustrate how successful and effective interaction is carried out to create the optimal conditions for second language acquisition.

Cooperative Search and Task Allocation Among Heterogeneous Teams of UAVs

A cooperative control method of multi-class UAV(unmanned air vehicle) team is investigated.During the mission,the UAVs perform search,classification,attack and battle damage assessment(BDA) tasks at various locations,which involves a combination of the team intelligence type of decision making combined with control,estimate and real-time trajectory optimization.The search-theoretic approach based on rate of return(ROR) maps is developed to get the cooperative search strategy.Templates are developed and views are combined to maximize the probability of correct target identification over various aspect angles.Monte Carle simulation runs for the scenario to evaluate the performance of the approach with various decision parameters,UAVs distributions and UAV team characteristics.Simulation results show that the cooperative behavior can significantly improve the operational effectiveness of UAV team,and the cooperative control allows for near optimal solution of the correlative behavior of a group of UAVs in battlefield.

Study on DSM-based task planning of product cooperative development

The results of analyzing the managerial characteristics and complexity of product cooperative development suggest that task planning is an important aspect for process management of product cooperative development and the method for planning tasks should be able to model the dependency between tasks and iterations during the development process. In this paper, a DSM-based method and its corresponding optimization algorithms are developed. At first the coupled task sets and uncoupled task sets are identified, and the tasks are then optimized by the corresponding algorithms. The optimal tasks plan will reduce the development time and cost. Considering the practical requirements in real world, a Multilayer DSM is proposed, and its information communication techniques between DSM and traversing principle are described in details.

Research and Study on Cooperative Learning with special Reference to Vocational Students of Practical English class

This paper covers an experimental study with the application of cooperative learning in the college English teaching to vocational students. It intends to find answers to the following questions: how do students cooperate in cooperative learning? Can cooperative learning promote students' learning? The author conducts an experiment by applying recording. Based on the above research work, this result has been reached: cooperative learning can promote students'mastering of vocabulary and grammar. The author would like to share her experiences with others in pedagogical studies of teaching vocational college students English.

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.

基于第三方工作任务的紧密型校企合作探索

良好的校企合作关系需要校、企双方共同维护,其中学校应该起主导作用。南京铁道职业技术学院城轨运营管理专业利用第三方工作任务,在查找安全隐患、解决安全难题、提升职工心理素质等方面为南京地铁运营公司提供了全面的运营安全咨询服务;南京地铁运营公司在获得专业服务的同时,也对学校的学生实习、教师实践、科研立项等工作给予有力支持。校企双方紧密合作,各取所需、互惠互利、相互促进,共同推进校企合作深入发展。

“钟摆逻辑”:有限结构化的村务工作模式——基于湖北S村的经验研究

我国村级组织具有复杂性,在村务工作的组织模式、规范要求和具体内容等层面存在较大差异。研究发现,基层的村务工作呈现出明显的“钟摆逻辑”,即面对常规任务和特殊任务采取不同的应对方式。在处理常规任务时,运用分工合作的模式,将任务进行条线分割,主要采用坐班制、固定工作场所及规则治理等手段;而面对特殊任务时,则结成“行动班子”,以团结合作、工作动员等方式来应对。这种在常态模式与动员模式之间的切换使得村务工作处于有限结构化的状态,有助于提升基层治理效能。

任务驱动和分组合作教学法在动物学实验中的应用

动物学实验课程是生命科学学院3个专业的一门必修专业基础课,为进一步提高课程教学效果,选取内江师范学院生命科学学院生物科学专业学生60人,随机分为试验组和对照组,分别应用任务驱动和分组合作教学法、传统实验教学法开展教学。实验结束后对2组学生进行问卷调查和考试测评。结果显示:试验组学生对学习积极性、独立思考能力,自主学习能力、理论联系实际能力、实验操作能力、小组沟通合作能力、师生互动和学生创新思维的认可度远高于对照组;试验组的理论平均成绩和实验平均成绩分别为80.15±6.25、84.64±6.78,高于对照组平均成绩75.45±5.12、72.68±7.36,呈显著性差异(p<0.05)。实验表明,任务驱动和分组合作教学法应用于动物学实验教学中效果良好,有助于创新型人才培养。

通信约束下UAV集群协同拦截任务分配算法

针对多无人机协同拦截多机动目标的任务分配问题,同时考虑到真实战场环境中存在的通信约束以及探测范围约束条件,本文提出了分步一致性拍卖算法(SCBAA)。首先,对真实战场环境中存在的通信约束以及探测范围约束等问题进行了描述分析,构建了多无人机协同拦截任务分配模型,设计了综合效能函数以及相应约束条件。其次,为解决多无人机协同打击单一目标的不平衡任务分配以及冲突消解问题,将原任务分配过程分为主要任务分配以及次要任务分配两部分,通过多次拍卖以及冲突消解实现多无人机对单一目标的任务分配。仿真结果表明,该算法可有效解决通信约束条件下的分布式多无人机协同拦截问题,并适应动态环境中任务分配对实时性的要求。

面向动态目标搜索与打击的空地协同自主任务分配方法

面向复杂城市环境中的运动目标搜索与打击任务,单域无人平台受视野范围和运动能力等限制,易出现目标遗漏和任务完成率低的问题。针对这些问题,提出一种面向动态目标搜索与打击的空地协同自主任务分配方法,通过结合无人机视野范围广以及无人车机动性强的特点,提升空地无人系统的任务执行效率和区域覆盖率。一方面,针对未知运动目标,提出一种基于数字信息素的目标搜索模型,以平台协同收益和区域覆盖率为优化指标,保证在尽可能短的时间周期内发现区域中的所有目标。另一方面,面向动态到达的打击任务,构建基于可行路径规划的任务分配模型,以平台能耗和任务完成时间为目标函数,在保证任务完成率的同时,提高空地协同系统的资源利用率。与现有方法相比,所提方法能够在最短的时间内发现所有目标,区域覆盖率达到55%以上,且资源利用率为84.4%。实验结果表明,所提方法具备较好的目标搜索和任务执行能力。

图多智能体任务建模视角下的协作子任务行为发现

大量多智能体任务都表现出近似可分解结构,其中相同交互集合中智能体间交互强度大,而不同交互集合中智能体间交互强度小.有效建模该结构并利用其来协调智能体动作选择可以提升合作型多智能体任务中多智能体强化学习算法的学习效率.然而,目前已有工作通常忽视并且无法有效实现这一目标.为解决该问题,使用动态图来建模多智能体任务中的近似可分解结构,并由此提出一种名叫协作子任务行为(coordinated subtask pattern,CSP)的新算法来增强智能体间局部以及全局协作.具体而言,CSP算法使用子任务来识别智能体间的交互集合,并利用双层策略结构来将所有智能体周期性地分配到多个子任务中.这种分配方式可以准确刻画动态图上智能体间的交互关系.基于这种子任务分配,CSP算法提出子任务内和子任务间行为约束来提升智能体间局部以及全局协作.这2种行为约束确保相同子任务内的部分智能体间可以预知彼此动作选择,同时所有智能体选择优异的联合动作来最大化整体任务性能.在星际争霸环境的多个地图上开展实验,实验结果表明CSP算法明显优于多种对比算法,验证了所提算法可以实现智能体间的高效协作.

基于改进黑猩猩算法的异构多无人机协同任务分配

针对异构多无人机对地面多类型固定目标的任务分配问题,提出一种基于协同多任务分配模型和改进黑猩猩算法的任务分配方法。通过对四元组内元素的设定完成模型构建,建立总收益最高、威胁代价最低、总航程最短的多目标评价函数。使用改进黑猩猩算法完成模型求解,引入混沌反向学习策略提升初始种群分布的多样性,在迭代阶段使用抢食行为动态调整策略,提升了跳出局部最优解的能力。实验表明:相比原算法,改进黑猩猩算法所得任务分配方案在保持较高任务收益和较低威胁代价的同时,在两种规模场景下的平均总航程分别降低了22.6%和21.1%。

基于深度强化学习的办公流程任务分配优化

在办公平台中存在异构流程任务大量并行的情况,不仅需要任务执行者具有较强的能力,也对协同调度系统的性能提出了要求。采用强化学习(RL)算法,结合协作配合度、松弛度等定量分析,并基于马尔可夫博弈理论提出多智能体博弈模型,实现以总体流程配合度和最大完工时间为优化目标的优化调度系统,提高了总体执行效率。以真实的业务系统流程作为实验场景,在相同的优化目标下,对比D3QN等3种深度强化学习(DRL)算法和基于蚁群的元启发式算法,验证了所提方法的有效性。

fNIRS超扫描技术的发展及研究热点

就fNIRS超扫描技术在心理学认知神经科学研究中取得的进展以及应用领域进行概述,主要包括模仿任务、协调任务、眼神交流、经济游戏、合作竞争和自然情景下的互动6方面.未来,fNIRS超扫描技术的发展方向包括提高空间分辨率、开发更精确的信号处理算法、与其他脑影像技术结合以获得更全面和准确的脑区功能信息.为研究脑功能调节的机制、诊断和治疗神经疾病、脑发育和老化、心理和行为的神经基础等神经科学研究和临床诊疗具有广泛的应用价值和意义.