基于非理想传感器的小型无人机群协同目标搜索
Swarm coordination of mini-UAVs for target search using imperfect sensors
摘要：
无人机在非结构化环境下的搜索任务中具有巨大的潜力。小型、轻型、低速和敏捷的无人机，如多旋翼平台，可以结合多种传感器，适用于在杂乱的户外区域检测感兴趣的物体。然而，由于其有限的耐力，中等的计算能力，和不完善的传感器，小型无人机应成组使用群协调算法，以执行任务的可扩展性，可靠性和鲁棒性的方式。本文提出了一种基于生物启发的机制来协调无人机在传感器不完善的情况下进行目标搜索。从本质上讲，协调可以通过结合污名和群集行为来实现。当无人机在感知到潜在目标时释放数字信息素时，就会发生Stigmergy。这样的信息素可以聚集和扩散之间的蜂群雄蜂，创造一个时空吸引力的潜在领域。集群的发生是对齐、分离和凝聚的一种紧急效应，无人机以相似的航向和动态排列作为一个群体进行自我组织。无人机的紧急协调依赖于污名和群集策略的一致性。本文报告了新的群集算法的设计，审查不同的策略，并测量其性能的一些合成和现实世界的情况。
英文摘要：
Unmanned Aerial Vehicles (UAVs) have a great potential to support search tasks in unstructured environments. Small, lightweight, low speed and agile UAVs, such as multi-rotors platforms can incorporate many kinds of sensors that are suitable for detecting object of interests in cluttered outdoor areas. However, due to their limited endurance, moderate computing power, and imperfect sensing, mini-UAVs should be into groups using swarm coordination algorithms to perform tasks in a scalable, reliable and robust manner. In this paper a biologically-inspired mechanisms is adopted to coordinate drones performing target search with imperfect sensors. In essence, coordination can be achieved by combining stigmergic and flocking behaviors. Stigmergy occurs when a drone releases digital pheromone upon sensing of a potential target. Such pheromones can be aggregated and diffused between flocking drones, creating a spatiotemporal attractive potential field. Flocking occurs, as an emergent effect of alignment, separation and cohesion, where drones self organise with similar heading and dynamic arrangement as a group. The emergent coordination of drones relies on the alignment of stigmergy and flocking strategies. This paper reports on the design of the novel swarming algorithm, reviewing different strategies and measuring their performance on a number of synthetic and real-world scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
