A distributed task reassignment method in dynamic environment for multi-UAV system

Applied Intelligence - This paper considers the task reassignment problem for distributed multiple Unmanned Aerial Vehicle (multi-UAV) systems in dynamic environment. For a dynamic reassignment...     

Decentralized task allocation for heterogeneous multi-UAV system with task coupling constraints

The Journal of Supercomputing - Cooperative multiple task assignment problem is an essential issue in the collaboration of multiple unmanned aerial vehicles (UAVs). Consensus-based bundle algorithm...     

多无人机航路重规划方法

提出了一种基于改进人工势场法的无人机航路重规划算法。在航路重规划中,设置多个节点,将无人机的航路分段,并重新构造斥力函数,无人机在突发威胁的边界调整航路,实时规划出避开突发威胁的可飞航路;仿真结果表明,该算法能够较好地满足航路规划的要求。     

针对动态目标的多无人机协同组合差分进化搜索方法

针对多无人机动态目标协同搜索问题,提出一种组合差分进化无人机协同搜索航迹规划方法.建立动态目标协同搜索环境信息图模型及无人机运动模型.基于改进差分蝙蝠算法和自适应差分进化算法,设计基于种群数量自适应分配的组合框架,将差分进化算法中的变异、交叉和选择机制引入蝙蝠算法,构建组合差分进化算法的协同搜索算法,并对无人机动态目标协同搜索的航迹进行优化.针对待搜索目标轨迹随机多变且具有规避侦察特性的现实场景,建立可回访数字信息图和自适应目标搜索增益函数,从而提高无人机对动态目标的捕获能力.最后,通过仿真结果验证所提出的无人机动态目标协同搜索算法的有效性.     

Two-way D algorithm for path planning and replanning

Inspired by the Witkowski’s algorithm, we introduce a novel path planning and replanning algorithm — the two-way D^∗ (TWD^∗) algorithm — based on a two-dimensional occupancy grid map of the environment. Unlike the Witkowski’s algorithm, which finds optimal paths only in binary occupancy grid maps, the TWD^∗ algorithm can find optimal paths in weighted occupancy grid maps. The optimal path found by the TWD^∗ algorithm is the shortest possible path for a given occupancy grid map of the environment. This path is more natural than the path found by the standard D^∗ algorithm as it consists of straight line segments with continuous headings. The TWD^∗ algorithm is tested and compared to the D^∗ and Witkowski’s algorithms by extensive simulations and experimentally on a Pioneer 3DX mobile robot equipped with a laser range finder.     

基于TL_Sheduling的异构集群负载均衡算法的研究与应用

异构环境下"集群系统"负载均衡技术的关键是实现进程的跨平台迁移,对于进程的跨平台迁移所带来的困难和开销是巨大的.在传统进程迁移算法基础上,充分考虑节点对提交作业的适应性,提出一种新的TL-Scheduling负载均衡算法,能够控制作业提交到适合它执行的节点上,该算法可以有效提高系统负载均衡和作业执行效率.在其基础上,研究设计了基于XML业务流程模板的卫星作业调度系统,使得作业调度系统更加实用化.     

Primitives for distributed computing in a heterogeneous local areanetwork environment

Epsilon is a testbed for monitoring distributed applications involving heterogeneous computers, including microcomputers, interconnected by a local area network. Such a hardware configuration is usual but raises difficulties for the programmer. First, the interprocess communication mechanisms provided by the operating systems are rather cumbersome to use. Second, they are different from one system to another. Third, the programmer of distributed applications should not worry about system and/or network aspects that are not relevant for the application level. The authors present the solution chosen in Epsilon. A set of high-level communication primitives has been designed and implemented to provide the programmer with an interface independent of the operating system and of the underlying interprocess communications facilities. A program participating in a distributed application can be executed on any host without any change in the source code except for host names     

引入兴趣稳定性的时间敏感协同过滤算法

针对物品流行偏置现象,将物品流行度引入到用户兴趣中建模,提出了基于物品流行度的用户兴趣特征相似度模型。针对传统模型没有考虑到用户兴趣稳定性和难以实时捕获用户兴趣问题,在计算用户兴趣相似度过程中引入时间敏感和兴趣稳定性,提出了引入时间敏感的用户兴趣稳定性的相似度模型。最后,融合权重因子将两种相似度模型线性加权,提出了一种新颖的IPSTS算法模型。实验表明,该算法相比传统协同过滤算法在平均绝对误差（MAE）和均方根误差（RMSE）上均有明显降低的同时也能挖掘出长尾物品,缓解了物品流行偏置现象。     

CPU-GPU异构多核系统的动态任务调度算法

CPU-GPU异构多核系统对计算密集型的应用加速效果显著而得到广泛应用,但该系统易出现负载均衡问题。针对此问题,本文提出了一种CPU-GPU异构多核系统的动态任务调度算法。该算法充分利用CPU的线程资源和GPU的计算资源,准确测量CPU和GPU的计算能力,从而动态调整分配到CPU和GPU上的数据块大小,减小负载的总执行时间,提高系统加速比。实验结果表明,该算法使得系统加速比提高34%~103%。     

异构环境中Fork-Join任务图的调度算法

目前已有的Fork-Join任务图的调度算法大多假定处理机为同构的,而没有考虑实际应用中处理机的异构性以及节省处理机的问题,导致算法在具体应用中效率较低.因此,对Fork-Join任务图的调度问题进行研究,提出了一个基于异构环境的贪心调度算法,该算法具有高的加速比和总体效率,其时间复杂度为O(v~2),其中,v表示任务集中任务的个数.实验结果表明,相比其它算法,该算法具有较短的调度长度、较短的完成时间,使用的处理机数较少,具有更强的实用性.     

云环境下异构无线传感器网络节点调度改进算法

针对大规模传感器网络节点异构性突出、调度困难,传统节点调度算法在大规模传感器网络环境下效率低等问题,提出了基于云计算的节点调度模型,并设计了该模型下的节点调度改进算法。通过将节点算法的执行迁移至云端,由云端调度节点。通过OMnet＋＋对改进算法与云端融合数据处理进行仿真实验表明：改进算法可以有效地延长网络生存周期,并且随着数据量的增加,云端处理融合数据耗时相对平稳。相比较传统节点调度算法,改进算法在能量与耗时指标上表现更优。     

面向动态环境的视觉惯性SLAM算法

针对传统的同时定位与建图(SLAM)算法在动态环境中会降低自身运动估计的精确性以及系统鲁棒性的问题,提出一种适用于动态环境的视觉惯性SLAM算法——DVI-SLAM(dynamic visual inertial SLAM).根据对极几何约束检测并去掉动态特征,利用更加精确的静态特征进行状态估计;添加视觉信息自适应权重...     

异构自组织网络环境下路由算法研究

针对传统自组织网络路由方法不能满足异构网络环境下的通信要求这一问题,提出混合节点发现算法(HNDA)以及结合路由跳数和节点负载的综合度量方法 (CM-HCTL);将HNDA与CM-HCTL相结合融入AODV协议,得出完整的异构网络环境下自组织网络路由解决方案HC-AODV。对AODV和HC-AODV这两种算法进行仿真对比,仿真结果表明,在一定的网络环境下,HC-AODV方法能够提高数据分组投递率,降低平均端到端时延以及路由控制开销,具有有效性。     

异构环境下增强的自适应MapReduce调度算法

针对Hadoop默认调度算法和异构环境下LATE调度算法的不足,在SAMR调度算法的基础上提出了一种增强的自适应MapReduce调度算法。该算法记录了每个节点的历史信息,采用K-means聚类算法动态地调整阶段进度值以找到真正需要启动备份的落后任务。实验结果表明,增强自适应的MapReduce调度算法在提高任务执行时间的估算误差以及准确识别慢任务方面具有一定的有效性。     

异构环境下的多子阵合成孔径声呐距离多普勒成像算法

为解决多子阵合成孔径声纳成像效率低的问题,提出了一种异构环境下的多子阵合成孔径声纳快速成像方法。根据多子阵合成孔径声纳距离多普勒成像算法特点以及CPU和GPU各自计算特点,通过将算法中距离向脉冲压缩、固定相位补偿、距离徙动校正和方位向脉冲压缩密集型运算采用GPU计算,极大提高了多子阵合成孔径声纳成像效率。最后通过实测数据的成像实验对所提算法的正确性和高效性进行了验证,与串行计算方法相比加速比高达14.45。     

Autonomous network-based integration architecture for multi-agent systems under dynamic and heterogeneous environment

Multi-agent systems fit nicely into domains that are naturally distributed and require artificial intelligence technology. Has been designed an autonomous information services integration architecture based on network to support the rapid changing environments and needs. However, substantial increase of users requests and redirects it may cause the system to unbalance loading and part overloading. This paper proposes an integrated access method by reduces the number of Pull Mobile Agents to reduces the total load of the system in order to achieveautonomous load distribution. In addition, the information structure of integrated service area is effective to improve the ratio of the satisfaction of Pull-Mas (Pull Mobile Agents) with joint request on one node. Through simulation tests show that this system can be guaranteed that services requests and related services requests is uniformly distributed to the nodes of system and ensure that the system load balancing.     

头脑风暴优化算法求解带转角能耗多无人机路径规划问题

多台无人机协同完成野外传感器数据采集的工作中,建立具有精确能耗模型的多无人机路径规划问题模型尤为重要。提出了带转角能耗多无人机路径规划问题(multi-UAV path planning with angular energy consumption, MUPP-AEC)模型,该模型考虑了无人机在加速、减速、匀速、转角等飞行条件下的能耗差异。针对MUPP-AEC的特点,提出目标空间聚类离散头脑风暴优化算法(discrete brain storm optimization algorithm in objective space, DBSO-OS)。该算法采用个体空间整数编码和带2-opt的分阶段贪婪法解码策略,并对扰动算子和个体更新算子进行了离散化定义。个体更新算子中采用了混合随机反转变换和部分匹配变换的生成策略。实验结果表明:DBSO-OS能有效地求解MUPP-AEC;所提离散头脑风暴算子在全局收敛能力、求解精度和稳定性等方面均优于传统头脑风暴算子;在中小规模测试算例和较大规模测试算例的测试中,DBSO-OS优于对比算法。     

Stackelberg-based dynamic incentive pricing algorithm in heterogeneous ubiquitous network

A challenge in the convergence of heterogeneous networks is how to combine the ubiquitous resources and provide the diversified individual services. This paper designs a market model for aggregating reconfiguration in heterogeneous networks based on the tradeoff between resource allocation and consumers’ requirement. To unify the benefits of operators and consumers, a novel Stackelberg-based dynamic incentive pricing algorithm is proposed. The results of the theoretical analysis and simulation demonstrate that the proposed strategy provides incentive for cooperation by means of appropriate resource allocation, and improves the utilization of network resources, thereby effectively realizing the optimization of the whole network performance.     

A fast predictive algorithm with idle reduction for heterogeneous system scheduling

A heterogeneous task scheduling algorithm called Predict and Arrange Task Scheduling (PATS) algorithm was proposed to achieve a lower bound time complexity with minimum schedule length. Two major steps were introduced, i.e. earliest finish time with level-based task scheduling and idle slot reduction. In the first step, tasks are scheduled according to their predicted earliest finish time from the candidate task list and their dependencies. Scheduling is performed one level at a time starting from top level and transcend downward. In the second step, the idle time slots in each processing unit are minimized. Two sets of experiments were designed to evaluate the merits of proposed algorithm. The first experiment involved the task graphs used by other methods. These graphs are all synthesized. The second experiment concerned the task graphs derived from real world applications such as montage work flow, molecular dynamic code. The experimental results showed that the PATS algorithm yielded better average schedule length ratio, running time, and efficiency than the compared algorithms.     

异构系统中的综合性启发式任务调度算法

任务的单个属性常作为基于优先驱动的表调度算法的优先级,针对这种方法常出现优先级相同的情况,提出一个综合性启发式算法HCPFS。算法分三个优先级选择任务进行调度,从高到低依次为：关键路径上的任务、就绪任务到出口任务的路径长度和后继任务数。调度过程中,算法采用任务复制和空闲时间区段任务插入的方法。采用随机生成图法和任务图集进行了算法模拟和比较,实验数据表明HCPFS算法具有更好的调度性能。