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Target Area Surveillance Optimization with Swarms of Autonomous Micro Aerial Vehicles

机译：带有自动微型飞行器群的目标区域监视优化

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Reconnaissance mission success within deep enemy territory or surveillance of a targeted remote area requiring fast and reliable data collection is many times a daunting task. The characteristics of particular missions dictate the area coverage, objects of interest, timing constraints, and last but not the least, the type of deployed system and its capabilities. The advances in unmanned aerial vehicles (UAV) over the past decade enhanced surveillance/reconnaissance/monitoring maneuvers for both military missions and civilian rescue operations. More recently, the UAV design and development has evolved also towards miniaturizing the actual aerial system and cooperation among a swarm of micro aerial vehicles (MAV). This work focuses on the optimization of a MAV swarm deployment in hostile territory. The reconnaissance mission success is given by the percentage of data collection within timing, distributed data storage, collision avoidance, and swarm MAV mission integrity constraints. A proof of concept simulation was built to evaluate the correctness of the swarm MAV model deployment and serves as the starting point for a larger deployment model simulation.
机译：在敌人深处进行侦察任务的成功或对目标偏远地区的监视需要快速而可靠的数据收集，这往往是一项艰巨的任务。特定任务的特征决定了区域覆盖范围，目标对象，时间限制以及最后但并非最不重要的部署系统的类型及其功能。过去十年来，无人机技术的进步增强了军事任务和民用救援行动的监视/侦察/监视机动性。最近，UAV的设计和开发也朝着使实际的空中系统小型化和微型飞行器（MAV）群之间的合作发展。这项工作的重点是在敌对领土上优化MAV群的部署。侦察任务的成功是由定时，分布式数据存储，避免碰撞以及大量MAV任务完整性约束内的数据收集百分比决定的。建立了概念验证仿真，以评估群体MAV模型部署的正确性，并作为更大的部署模型仿真的起点。

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