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A chemotactic pollution-homing UAV guidance system

机译：趋化污染归巢无人机制导系统

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Due to their deployment flexibility, Unmanned Aerial Vehicles have been found suitable for many application areas, one of them being air pollution monitoring. In fact, deploying a fleet of Unmanned Aerial Vehicles (UAVs) and using them to take environmental samples is an approach that has the potential to become one of the key enabling technologies to enforce pollution control in industrial or rural areas. In this paper, we propose to use an algorithm called Pollution-driven UAV Control (PdUC) that is based on a chemotaxis metaheuristic and a Particle Swarm Optimization (PSO) scheme that only uses local information. Our approach will be used by a monitoring Unmanned Aerial Vehicle to swiftly cover an area and map the distribution of its aerial pollution. We show that, when using PdUC, an implicit priority is applied in the construction of pollution maps, by focusing on areas where the pollutants' concentration is higher. In this way, accurate maps can be constructed in a faster manner when compared to other strategies. We compare PdUC against various standard mobility models through simulation, showing that our protocol achieves better performances, by finding the most polluted areas with more accuracy, within the time bounds defined by the UAV flight time.
机译：由于其部署的灵活性，已发现无人飞行器适用于许多应用领域，其中之一是空气污染监测。实际上，部署一支无人飞行器（UAV）机群并使用它们来进行环境采样是一种有可能成为在工业或农村地区实施污染控制的关键使能技术之一的方法。在本文中，我们建议使用一种称为污染驱动的无人机控制（PdUC）的算法，该算法基于趋化性元启发式算法和仅使用局部信息的粒子群优化（PSO）方案。监控无人机将使用我们的方法迅速覆盖一个区域并绘制其空气污染的分布图。我们表明，当使用PdUC时，通过关注污染物浓度较高的区域，隐式优先级将应用于污染图的构建。这样，与其他策略相比，可以以更快的方式构建准确的地图。通过仿真，我们将PdUC与各种标准机动性模型进行了比较，表明我们的协议通过在无人机飞行时间定义的时间范围内找到更准确的污染最严重的区域，从而实现了更好的性能。

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