In view of the structure and action behavior of mechatronic systems,a method of searching fault propagation paths called maximum-probability path search(MPPS)is proposed,aiming to determine all possible failure propag...In view of the structure and action behavior of mechatronic systems,a method of searching fault propagation paths called maximum-probability path search(MPPS)is proposed,aiming to determine all possible failure propagation paths with their lengths if faults occur.First,the physical structure system,function behavior,and complex network theory are integrated to define a system structural-action network(SSAN).Second,based on the concept of SSAN,two properties of nodes and edges,i.e.,the topological property and reliability property,are combined to define the failure propagation property.Third,the proposed MPPS model provides all fault propagation paths and possible failure rates of nodes on these paths.Finally,numerical experiments have been implemented to show the accuracy and advancement compared with the methods of Function Space Iteration(FSI)and the algorithm of Ant Colony Optimization(ACO).展开更多
Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical ...Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.展开更多
This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission...This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission area, the targets can intermittently obtain the position information of the UAVs from sensing devices, and take appropriate actions to increase the distance between themselves and the UAVs. Aiming at this problem, an environment model is established using the search map, and the updating method of the search map is extended by considering the sensing capabilities of the moving targets. A multi-UAV search path planning optimization model based on the model predictive control (MPC) method is constructed, and a hybrid particle swarm optimization algorithm with a crossover operator is designed to solve the model. Simulation results show that the proposed method can effectively improve the cooperative search efficiency and can find more targets per unit time compared with the coverage search method and the random search method.展开更多
针对跳点搜索算法(jump point search,JPS)在路径规划过程中出现的穿越墙角的不安全行为,提出了一种基于蜂窝栅格地图的跳点搜索算法(honeycomb raster map-JPS,H-JPS)。构建蜂窝栅格地图代替传统栅格地图,在JPS算法的基础上结合蜂窝栅...针对跳点搜索算法(jump point search,JPS)在路径规划过程中出现的穿越墙角的不安全行为,提出了一种基于蜂窝栅格地图的跳点搜索算法(honeycomb raster map-JPS,H-JPS)。构建蜂窝栅格地图代替传统栅格地图,在JPS算法的基础上结合蜂窝栅格修改了剪枝规则与跳点判断规则,再利用蜂窝栅格特点设计了新的启发式函数来提高搜索效率,通过找寻最远节点的节点更新规则来优化生成的轨迹。利用Matlab仿真平台验证算法的搜索效率和安全性,结果表明,相较于传统JPS算法,采用H-JPS算法进行路径规划能够完全消除危险节点,路径规划时间和长度分别缩短了41.9%和11.1%,显著提高了搜索效率。展开更多
针对传统蚁群算法在农机导航路径规划中存在前期搜索盲目、死锁、收敛速度慢、收敛路径质量低的问题,本文提出基于跳点优化蚁群算法(Jump point optimized ant colony algorithm,JPOACO)的路径规划方法。首先,使用优化跳点搜索算法对地...针对传统蚁群算法在农机导航路径规划中存在前期搜索盲目、死锁、收敛速度慢、收敛路径质量低的问题,本文提出基于跳点优化蚁群算法(Jump point optimized ant colony algorithm,JPOACO)的路径规划方法。首先,使用优化跳点搜索算法对地图进行预处理,获得简化跳点;其次,通过简化跳点对栅格地图进行信息素初始化,以加强简化跳点的引导能力和减少前期盲目搜索;接着,设计蚂蚁死亡惩罚机制,以降低陷入死锁蚂蚁走过路径的信息素,减少死锁问题的发生;再者,通过重新设计启发式信息函数并引入分级式信息素因子改进状态转移概率函数,以提高收敛速度,缩短路径长度;最后,采用路径优化策略删减不必要路径节点,以进一步缩短路径长度、提升平滑度,提高路径质量。仿真结果表明,在简单环境中,JPOACO算法求得的路径长度较传统蚁群算法和另一种优化蚁群算法短约22.6%和2.0%,收敛迭代次数、收敛时间分别减少约77.0%、77.5%和49.3%、87.8%,零死亡迭代次数和零死亡时间较后者减少约19.5%和80.5%;在复杂菠萝种植环境中,JPOACO算法较传统蚁群算法和另一种优化蚁群算法求得的路径长度短16.6%和4.7%,收敛迭代次数、收敛时间分别减少约77.1%、17.4%和73.7%、47.4%,零死亡迭代次数和零死亡时间较后者减少约34.3%和58.2%,表明本文算法具有较高的适用性和可行性。展开更多
利用出行特征数据识别综合交通运输通道是合理布局城市群综合运输通道的关键技术。本文基于城市群手机信令数据,提出一种综合运输通道识别四阶段方法框架,即数据准备、运输方式划分、最短路径搜索和通道识别。在运输方式划分方面,提出...利用出行特征数据识别综合交通运输通道是合理布局城市群综合运输通道的关键技术。本文基于城市群手机信令数据,提出一种综合运输通道识别四阶段方法框架,即数据准备、运输方式划分、最短路径搜索和通道识别。在运输方式划分方面,提出一种以运输平均速度和站点POI (Point of Interest)位置为决策变量的高速铁路、普速铁路和公路多方式划分算法。在最短路搜索方面,设计一种基于双向A*算法的最短路径搜索算法。在通道识别方面,基于行政边界划分通道区段并以运输量为综合运输通道区段判别参数。以京津冀城市群为例进行实证分析,结果表明,本文方法能够有效处理城市群手机信令数据,并识别出6条综合运输通道,验证了方法的可行性和准确性。在案例数据下,京津冀城市群公路和铁路的运输量占比分别为81.87%和18.13%,公路的短程运输客流较铁路更多;节假日因素显著提高了综合运输通道的客流量,平均运输量增加62.6%,平均客流周转量提升61.2%。展开更多
基金Project(2017JBZ103)supported by the Fundamental Research Funds for the Central Universities,China
文摘In view of the structure and action behavior of mechatronic systems,a method of searching fault propagation paths called maximum-probability path search(MPPS)is proposed,aiming to determine all possible failure propagation paths with their lengths if faults occur.First,the physical structure system,function behavior,and complex network theory are integrated to define a system structural-action network(SSAN).Second,based on the concept of SSAN,two properties of nodes and edges,i.e.,the topological property and reliability property,are combined to define the failure propagation property.Third,the proposed MPPS model provides all fault propagation paths and possible failure rates of nodes on these paths.Finally,numerical experiments have been implemented to show the accuracy and advancement compared with the methods of Function Space Iteration(FSI)and the algorithm of Ant Colony Optimization(ACO).
文摘Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.
基金supported by the National Natural Science Foundation of China(7140104871671059)the National Natural Science Funds of China for Innovative Research Groups(71521001)
文摘This paper studies the problem of using multiple unmanned air vehicles (UAVs) to search for moving targets with sensing capabilities. When multiple UAVs (multi-UAV) search for a number of moving targets in the mission area, the targets can intermittently obtain the position information of the UAVs from sensing devices, and take appropriate actions to increase the distance between themselves and the UAVs. Aiming at this problem, an environment model is established using the search map, and the updating method of the search map is extended by considering the sensing capabilities of the moving targets. A multi-UAV search path planning optimization model based on the model predictive control (MPC) method is constructed, and a hybrid particle swarm optimization algorithm with a crossover operator is designed to solve the model. Simulation results show that the proposed method can effectively improve the cooperative search efficiency and can find more targets per unit time compared with the coverage search method and the random search method.
文摘针对跳点搜索算法(jump point search,JPS)在路径规划过程中出现的穿越墙角的不安全行为,提出了一种基于蜂窝栅格地图的跳点搜索算法(honeycomb raster map-JPS,H-JPS)。构建蜂窝栅格地图代替传统栅格地图,在JPS算法的基础上结合蜂窝栅格修改了剪枝规则与跳点判断规则,再利用蜂窝栅格特点设计了新的启发式函数来提高搜索效率,通过找寻最远节点的节点更新规则来优化生成的轨迹。利用Matlab仿真平台验证算法的搜索效率和安全性,结果表明,相较于传统JPS算法,采用H-JPS算法进行路径规划能够完全消除危险节点,路径规划时间和长度分别缩短了41.9%和11.1%,显著提高了搜索效率。
文摘针对传统蚁群算法在农机导航路径规划中存在前期搜索盲目、死锁、收敛速度慢、收敛路径质量低的问题,本文提出基于跳点优化蚁群算法(Jump point optimized ant colony algorithm,JPOACO)的路径规划方法。首先,使用优化跳点搜索算法对地图进行预处理,获得简化跳点;其次,通过简化跳点对栅格地图进行信息素初始化,以加强简化跳点的引导能力和减少前期盲目搜索;接着,设计蚂蚁死亡惩罚机制,以降低陷入死锁蚂蚁走过路径的信息素,减少死锁问题的发生;再者,通过重新设计启发式信息函数并引入分级式信息素因子改进状态转移概率函数,以提高收敛速度,缩短路径长度;最后,采用路径优化策略删减不必要路径节点,以进一步缩短路径长度、提升平滑度,提高路径质量。仿真结果表明,在简单环境中,JPOACO算法求得的路径长度较传统蚁群算法和另一种优化蚁群算法短约22.6%和2.0%,收敛迭代次数、收敛时间分别减少约77.0%、77.5%和49.3%、87.8%,零死亡迭代次数和零死亡时间较后者减少约19.5%和80.5%;在复杂菠萝种植环境中,JPOACO算法较传统蚁群算法和另一种优化蚁群算法求得的路径长度短16.6%和4.7%,收敛迭代次数、收敛时间分别减少约77.1%、17.4%和73.7%、47.4%,零死亡迭代次数和零死亡时间较后者减少约34.3%和58.2%,表明本文算法具有较高的适用性和可行性。
文摘利用出行特征数据识别综合交通运输通道是合理布局城市群综合运输通道的关键技术。本文基于城市群手机信令数据,提出一种综合运输通道识别四阶段方法框架,即数据准备、运输方式划分、最短路径搜索和通道识别。在运输方式划分方面,提出一种以运输平均速度和站点POI (Point of Interest)位置为决策变量的高速铁路、普速铁路和公路多方式划分算法。在最短路搜索方面,设计一种基于双向A*算法的最短路径搜索算法。在通道识别方面,基于行政边界划分通道区段并以运输量为综合运输通道区段判别参数。以京津冀城市群为例进行实证分析,结果表明,本文方法能够有效处理城市群手机信令数据,并识别出6条综合运输通道,验证了方法的可行性和准确性。在案例数据下,京津冀城市群公路和铁路的运输量占比分别为81.87%和18.13%,公路的短程运输客流较铁路更多;节假日因素显著提高了综合运输通道的客流量,平均运输量增加62.6%,平均客流周转量提升61.2%。
