无人机集群协同对抗论文与程序资源分享

版权申诉
5星 · 超过95%的资源 63 下载量 62 浏览量 更新于2024-11-05 12 收藏 909KB RAR 举报
资源摘要信息:"2020华为杯研究生数学建模国赛D题第一问论文+程序"是一份关于2020年华为杯研究生数学建模竞赛中D题的论文与程序的集合资源。竞赛中的D题主要探讨了无人机集群协同对抗的策略与实施问题,这个问题在无人机技术日益成熟的今天,对于优化无人机集群行为和提高协同效率具有重要意义。本资源中所包含的内容不仅有在该项竞赛中获得国二等奖项(排名36)的完整论文,还包含了实现集群突防的相关程序代码,以及用于展示可成功突防区域边界散点图的程序。这些资源对于研究无人机协同作战、优化控制算法、以及在人工速度势场方面的研究具有重要的参考价值。 论文部分详细介绍了研究的背景、目标以及所采取的方法。在描述部分提到,“无人机协同对抗”是论文所解决的问题,该问题涉及无人机集群在复杂环境下的路径规划、任务分配、威胁评估、避障与协同突防等关键技术。论文中采用了数学建模的方法,通过建立数学模型来模拟无人机集群的行为,并对其性能进行分析评估。由于该论文在比赛中成绩突出,因此可以作为该领域研究的一个参考。 程序部分包含了两部分主要功能的代码:一是集群突防程序,二是用于绘制可成功突防区域边界散点图的程序。集群突防程序可能采用了动态规划、遗传算法、粒子群优化或其他启发式算法来求解无人机集群的最优突防策略,以提高在遭遇敌方防御时的成功概率。绘制散点图的程序则可能利用了数据可视化技术,通过分析和处理无人机的飞行数据,将成功突防的区域用散点图的形式直观展示出来,这有助于进一步分析和调整无人机的行动策略。 标签中提到的“人工速度势场”可能是指在无人机集群协同对抗中采用的一种技术或理论,用于模拟无人机之间的相互作用力。在势场理论中,每个无人机都被视作具有某种“势”,这种势与其它无人机的势相互作用产生速度变化,从而指导无人机的飞行轨迹。这在规划多无人机协同任务时非常有用,尤其是在需要考虑无人机间避碰和协作的情况下。 在“2020研究生数学建模D题 华为杯第十七届研究生数学建模竞赛”标签中可以了解到,这是一场面向研究生的高水平数学建模竞赛,而D题是其中的一个比赛题目。参与此类竞赛不仅要求参赛者具备扎实的数学建模能力,还需要对无人机技术、人工智能算法、计算机编程等方面有深入的了解和实践。 通过这些文件内容的介绍,我们可以看到,无人机集群协同对抗是一个综合性很强的研究方向,涉及到多个学科的知识和技术。通过建模和程序实现,不仅可以提高无人机的作战效率,也可以在民用领域如无人机快递、农业监测、灾难救援等方面得到广泛应用。这份资源对于想要了解和深入研究无人机集群协同对抗的学者和工程师来说,无疑是一个宝贵的参考资料。
2020-05-08 上传
summary: In this paper, we establish a regression model based on the passing network to evaluate the influence of team structure strategy and opponents’ counter-strategy on the match results. Fortask1,wefirstlistsomeHuskiesmatchstatisticsforthisseasonandanalyzetheteamin brief. Secondly, we construct a passingnetwork based on the number of passes and visualizes the passing network diagram of three games under three different coaches. We use these three diagrams to describe and compare the changes in Huskies’ strategies. After that, we identify network patterns of dyadic and triadic configurations and count 15 kinds of these two configurations in the above three matches, reflecting the structural indicators of the passing network. We also explore time scale and micro scale by giving the change of the team’s centroid over time in the first match and the Huskies’ 4 positions heat map over the season. For task 2, we construct the regression model not only introducing the basic data representing Huskies’ and opponents’ ability, but also extracting six independent variables from the indicators of the passing network into the model. Considering opponents’ counter-strategies, we also introduce the product interaction term between opponents’ data and network structure indicators. Through the training of regression model, we can judge whether the independent variables introduced have influence, what kind of influence and how much influence the independent variables introduced have on the result of the match. For task 3, by bringing in data for training, the model leaves 10 variables including interactionterms. Inordertoverifytheaccuracyofthemodel,weuseLeaveOneOutcrossvalidation, andthepredictedaccuracyoftheraceresultreached71.05%. Then,basedonthetrainedmodel, we point out the effective structural strategies Huskies currently have, such as the strong connection between the core players. Meanwhile, we also give specific advice for Huskies team to improve team success, such as the emphasis on triadic configurations among players. Fortask4,weextendthemodelappliedtohuskiestoallteamworkscenariosandintroduce the IPOI model. The IPOI model conducts multi-level induction of influencing factors and selection of assessment indicators from the four aspects of team input, process, output and reinput, taking into account team construction, operation, management, feedback and other aspects. WeconsiderthattheexistingHuskiemodelisprogresspartofIPOImodel,andweadd the evaluation system of input, output and reinput part, taking the university scientific research team modeling as an example. Insummary,ourmodelispracticalandreliableforhandlingnetwork-basedteamworkproblems in society. Keywords: football strategy, network science, regression analysis, IPOI model.