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A dddams-based UAV and UGV team formation approach for surveillance and crowd control

机译:基于dddams的UAV和UGV团队形成方法,用于监视和人群控制

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The goal of this paper is to study the team formation of multiple UAVs and UGVs for collaborative surveillance and crowd control under uncertain scenarios (e.g. crowd splitting). A comprehensive and coherent dynamic data driven adaptive multi-scale simulation (DDDAMS) framework is adopted, with the focus on simulation-based planning and control strategies related to the surveillance problem considered in this paper. To enable the team formation of multiple UAVs and UGVs, a two stage approach involving 1) crowd clustering and 2) UAV/UGV team assignment is proposed during the system operations by considering the geometry of the crowd clusters and solving a multi-objective optimization problem. For the experiment, an integrated testbed has been developed based on agent-based hardware-in-the-loop simulation involving seamless communications among simulated and real vehicles. Preliminary results indicate the effectiveness and efficiency of the proposed approach for the team formation of multiple UAVs and UGVs.
机译:本文的目的是研究在不确定情况下(例如人群拆分)的多个UAV和UGV的团队组成,以进行协作监视和人群控制。采用了一个综合,协调的动态数据驱动的自适应多尺度仿真(DDDAMS)框架,重点是与本文所考虑的监视问题相关的基于仿真的计划和控制策略。为了使多个UAV和UGV形成团队,提出了一种两阶段方法,包括1)人群聚类和2)UAV / UGV团队分配,该方法通过考虑人群聚类的几何形状并解决多目标优化问题来进行。对于实验,已经开发了基于基于代理的硬件在环仿真的集成测试平台,该仿真涉及仿真和真实车辆之间的无缝通信。初步结果表明,所提出的方法对多个无人机和无人飞行器的团队形成的有效性和效率。

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