首页> 外文会议>Sarnoff Symposium (SARNOFF), 2012 35th IEEE >Particle swarm optimization based topology control mechanism for holonomic unmanned vehicles operating in three-dimensional space
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Particle swarm optimization based topology control mechanism for holonomic unmanned vehicles operating in three-dimensional space

机译:基于粒子群优化的三维空间无人飞行器拓扑控制机制

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Wireless sensor networks (WSNs) are increasingly being used to acquire information in harsh and inaccessible environments including underwater and aerial theatres. Self-deployment of autonomous mobile assets in three-dimensional (3D) environments is a critical task to efficiently create WSNs. In this paper, we present a topology control mechanism called 3D-PSO, based on particle swarm optimization algorithm (PSO) running in holonomic unmanned vehicles (HUVs) operating in 3D space. Each HUV is able to adjust its speed and movement direction to achieve better fitness location using our 3D-PSO. We demonstrate that 3D-PSO uses limited information collected from each HUVs local neighbors to make movement decisions over an unknown 3D space to obtain a uniform spatial distribution while maintaining network connectivity. Simulation experiments demonstrate that parameters such as the number of iterations used in our 3D-PSO, and acceleration coefficients influence the efficiency of 3D-PSO.
机译:无线传感器网络(WSN)越来越多地用于在恶劣和不可访问的环境(包括水下和空中剧院)中获取信息。在三维(3D)环境中自我部署自主移动资产是有效创建WSN的关键任务。在本文中,我们基于在3D空间中运行的完整无人飞行器(HUV)中运行的粒子群优化算法(PSO),提出了一种称为3D-PSO的拓扑控制机制。每个HUV都可以使用我们的3D-PSO调整其速度和运动方向,以达到更好的健身位置。我们证明3D-PSO使用从每个HUV本地邻居收集的有限信息来在未知3D空间上做出运动决策,以在保持网络连接性的同时获得均匀的空间分布。仿真实验表明,诸如3D-PSO中使用的迭代次数之类的参数以及加速度系数会影响3D-PSO的效率。

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