在多无人机协同突防过程中,针对无人机需完成目标分配与探干侦动作选择的任务决策存在模型构建难,对模型求解方法复杂度高的问题,提出一种蒙特卡罗树搜索(Monte Carlo tree search,MCTS)的改进方法实现多无人机多任务联合决策。首先,考...在多无人机协同突防过程中,针对无人机需完成目标分配与探干侦动作选择的任务决策存在模型构建难,对模型求解方法复杂度高的问题,提出一种蒙特卡罗树搜索(Monte Carlo tree search,MCTS)的改进方法实现多无人机多任务联合决策。首先,考虑无人机与雷达对抗中的角度、距离等态势要素,以及当前无人机动作执行成功概率和雷达状态有效概率,构建多无人机目标分配与探干侦动作统一决策数学模型。其次,提出搜索次数自适应调整的改进MCTS算法对模型求解,实现大规模解空间在线快速寻优。仿真结果表明,改进算法使多雷达系统对无人机的威胁程度下降约16.8%,相比多臂赌博机算法效果提升约5.08%,决策时间约0.23 s,比传统MCTS缩短约45.7%,有助于提升无人机战场生存率。展开更多
针对低空经济背景下无人机在复杂三维建筑环境中的路径规划需求,提出改进的双向快速搜索树自适应交替双目标偏差搜索(Sampling-Tree Based bidirectional Rapidly-exploring Random Tree,ST-BA-RRT)算法。该算法在采样阶段采用三维环境...针对低空经济背景下无人机在复杂三维建筑环境中的路径规划需求,提出改进的双向快速搜索树自适应交替双目标偏差搜索(Sampling-Tree Based bidirectional Rapidly-exploring Random Tree,ST-BA-RRT)算法。该算法在采样阶段采用三维环境下的椭球采样,并配合双目标偏差策略抑制随机树向障碍区扩展,定向引导其向目标生长;扩展阶段运用自适应交替探索与改进人工势场辅助策略,增强算法环境适应性与局部避障能力。碰撞检测环节通过设计新型代价函数减少障碍物检查频次,优化规划时间;连通性处理利用双向随机采样提升规划效率;最后借助β样条平滑路径。实验结果表明,相较于现有算法,ST-BA-RRT算法生成的路径更短、更平滑,路径规划时间平均减少35%,在路径质量与环境适应性方面优势显著,能够高效生成优化飞行轨迹,满足复杂三维建筑环境下无人机路径规划需求。展开更多
Based on a thorough theory of the Artin transfer homomorphism from a group G to the abelianization of a subgroup of finite index , and its connection with the permutation representation and the monomial representation...Based on a thorough theory of the Artin transfer homomorphism from a group G to the abelianization of a subgroup of finite index , and its connection with the permutation representation and the monomial representation of G, the Artin pattern , which consists of families , resp. , of transfer targets, resp. transfer kernels, is defined for the vertices of any descendant tree T of finite p-groups. It is endowed with partial order relations and , which are compatible with the parent-descendant relation of the edges of the tree T. The partial order enables termination criteria for the p-group generation algorithm which can be used for searching and identifying a finite p-group G, whose Artin pattern is known completely or at least partially, by constructing the descendant tree with the abelianization of G as its root. An appendix summarizes details concerning induced homomorphisms between quotient groups, which play a crucial role in establishing the natural partial order on Artin patterns and explaining the stabilization, resp. polarization, of their components in descendant trees T of finite p-groups.展开更多
Urban grid power forecasting is one of the important tasks of power system operators, which helps to analyze the development trend of the city. As the demand for electricity in various industries is affected by many f...Urban grid power forecasting is one of the important tasks of power system operators, which helps to analyze the development trend of the city. As the demand for electricity in various industries is affected by many factors, the data of relevant influencing factors are scarce, resulting in great deviations in the accuracy of prediction results. In order to improve the prediction results, this paper proposes a model based on Multi-Target Tree Regression to predict the monthly electricity consumption of different industrial structures. Due to few data characteristics of actual electricity consumption in Shanghai from 2013 to the first half of 2017. Thus, we collect data on GDP growth, weather conditions, and tourism season distribution in various industries in Shanghai, model and train the electricity consumption data of different industries in different months. The multi-target tree regression model was tested with actual values to verify the reliability of the model and predict the monthly electricity consumption of each industry in the second half of 2017. The experimental results show that the model can accurately predict the monthly electricity consumption of various industries.展开更多
针对基本的快速搜索随机树(rapidly-exploring random tree,RRT)算法用于路径规划时存在的树扩展无导向性、密集障碍物区域规划效率低、局部区域节点聚集等问题,提出一种新的RRT改进算法。该算法采用增强的目标偏向策略,并引入可变的权...针对基本的快速搜索随机树(rapidly-exploring random tree,RRT)算法用于路径规划时存在的树扩展无导向性、密集障碍物区域规划效率低、局部区域节点聚集等问题,提出一种新的RRT改进算法。该算法采用增强的目标偏向策略,并引入可变的权值系数,提高随机树扩展的导向性和灵活性;同时采用局部节点过滤机制,过滤局部区域内聚集的节点;最后,使用节点直连策略对初始路径进行优化处理。仿真实验的结果表明,改进的RRT算法规划路径的速度更快且生成的路径质量更高,充分证明了改进算法的有效可行性。展开更多
针对传统RRT(Rapidly-exploring Random Tree)算法在进行机械臂路径规划时存在的采样随机性过大、搜索效率低下、所规划的路径曲折等问题,提出一种基于采样区域限制的改进RRT(Sampling Area Restriction RRT,SAR-RRT)算法。首先,针对随...针对传统RRT(Rapidly-exploring Random Tree)算法在进行机械臂路径规划时存在的采样随机性过大、搜索效率低下、所规划的路径曲折等问题,提出一种基于采样区域限制的改进RRT(Sampling Area Restriction RRT,SAR-RRT)算法。首先,针对随机性过大的问题,通过引入目标偏置策略来增强随机树的目标导向性,并采用球形采样区域以及角度限制策略对算法的采样进行约束,减少算法对无用空间区域的探索。其次,为提升算法的搜索效率,对随机树的节点扩展进行自适应优化,采用多步长扩展,使算法能够充分利用环境与障碍物的信息,同时利用贪婪思想加快随机树的收敛从而缩短路径的生成时间。最后,对初始规划出的路径进行二次优化处理,在去除路径中的冗余点后以三次B样条曲线对路径进行平滑处理,提升所规划路径的质量。实验结果表明,在2维及3维场景下,SAR-RRT算法均可以顺利完成路径规划任务。对比传统RRT算法,改进算法总体上使路径长度降低27.73%,规划时间缩短85.25%,采样点数减少87.19%且所生成的路径更加平滑。展开更多
文摘在多无人机协同突防过程中,针对无人机需完成目标分配与探干侦动作选择的任务决策存在模型构建难,对模型求解方法复杂度高的问题,提出一种蒙特卡罗树搜索(Monte Carlo tree search,MCTS)的改进方法实现多无人机多任务联合决策。首先,考虑无人机与雷达对抗中的角度、距离等态势要素,以及当前无人机动作执行成功概率和雷达状态有效概率,构建多无人机目标分配与探干侦动作统一决策数学模型。其次,提出搜索次数自适应调整的改进MCTS算法对模型求解,实现大规模解空间在线快速寻优。仿真结果表明,改进算法使多雷达系统对无人机的威胁程度下降约16.8%,相比多臂赌博机算法效果提升约5.08%,决策时间约0.23 s,比传统MCTS缩短约45.7%,有助于提升无人机战场生存率。
文摘针对低空经济背景下无人机在复杂三维建筑环境中的路径规划需求,提出改进的双向快速搜索树自适应交替双目标偏差搜索(Sampling-Tree Based bidirectional Rapidly-exploring Random Tree,ST-BA-RRT)算法。该算法在采样阶段采用三维环境下的椭球采样,并配合双目标偏差策略抑制随机树向障碍区扩展,定向引导其向目标生长;扩展阶段运用自适应交替探索与改进人工势场辅助策略,增强算法环境适应性与局部避障能力。碰撞检测环节通过设计新型代价函数减少障碍物检查频次,优化规划时间;连通性处理利用双向随机采样提升规划效率;最后借助β样条平滑路径。实验结果表明,相较于现有算法,ST-BA-RRT算法生成的路径更短、更平滑,路径规划时间平均减少35%,在路径质量与环境适应性方面优势显著,能够高效生成优化飞行轨迹,满足复杂三维建筑环境下无人机路径规划需求。
文摘Based on a thorough theory of the Artin transfer homomorphism from a group G to the abelianization of a subgroup of finite index , and its connection with the permutation representation and the monomial representation of G, the Artin pattern , which consists of families , resp. , of transfer targets, resp. transfer kernels, is defined for the vertices of any descendant tree T of finite p-groups. It is endowed with partial order relations and , which are compatible with the parent-descendant relation of the edges of the tree T. The partial order enables termination criteria for the p-group generation algorithm which can be used for searching and identifying a finite p-group G, whose Artin pattern is known completely or at least partially, by constructing the descendant tree with the abelianization of G as its root. An appendix summarizes details concerning induced homomorphisms between quotient groups, which play a crucial role in establishing the natural partial order on Artin patterns and explaining the stabilization, resp. polarization, of their components in descendant trees T of finite p-groups.
文摘Urban grid power forecasting is one of the important tasks of power system operators, which helps to analyze the development trend of the city. As the demand for electricity in various industries is affected by many factors, the data of relevant influencing factors are scarce, resulting in great deviations in the accuracy of prediction results. In order to improve the prediction results, this paper proposes a model based on Multi-Target Tree Regression to predict the monthly electricity consumption of different industrial structures. Due to few data characteristics of actual electricity consumption in Shanghai from 2013 to the first half of 2017. Thus, we collect data on GDP growth, weather conditions, and tourism season distribution in various industries in Shanghai, model and train the electricity consumption data of different industries in different months. The multi-target tree regression model was tested with actual values to verify the reliability of the model and predict the monthly electricity consumption of each industry in the second half of 2017. The experimental results show that the model can accurately predict the monthly electricity consumption of various industries.
文摘针对基本的快速搜索随机树(rapidly-exploring random tree,RRT)算法用于路径规划时存在的树扩展无导向性、密集障碍物区域规划效率低、局部区域节点聚集等问题,提出一种新的RRT改进算法。该算法采用增强的目标偏向策略,并引入可变的权值系数,提高随机树扩展的导向性和灵活性;同时采用局部节点过滤机制,过滤局部区域内聚集的节点;最后,使用节点直连策略对初始路径进行优化处理。仿真实验的结果表明,改进的RRT算法规划路径的速度更快且生成的路径质量更高,充分证明了改进算法的有效可行性。
文摘针对传统RRT(Rapidly-exploring Random Tree)算法在进行机械臂路径规划时存在的采样随机性过大、搜索效率低下、所规划的路径曲折等问题,提出一种基于采样区域限制的改进RRT(Sampling Area Restriction RRT,SAR-RRT)算法。首先,针对随机性过大的问题,通过引入目标偏置策略来增强随机树的目标导向性,并采用球形采样区域以及角度限制策略对算法的采样进行约束,减少算法对无用空间区域的探索。其次,为提升算法的搜索效率,对随机树的节点扩展进行自适应优化,采用多步长扩展,使算法能够充分利用环境与障碍物的信息,同时利用贪婪思想加快随机树的收敛从而缩短路径的生成时间。最后,对初始规划出的路径进行二次优化处理,在去除路径中的冗余点后以三次B样条曲线对路径进行平滑处理,提升所规划路径的质量。实验结果表明,在2维及3维场景下,SAR-RRT算法均可以顺利完成路径规划任务。对比传统RRT算法,改进算法总体上使路径长度降低27.73%,规划时间缩短85.25%,采样点数减少87.19%且所生成的路径更加平滑。
