RI: Adaptive Motion Coordination of Multiple Unmanned Aerial Vehicles for the Monitoring of Aerosol-Dust-Cloud-Radiation Interactions

该项目的主要目标是开发可扩展的、系统的方法来综合移动传感器网络的自适应采样策略。特别是，在确定研究挑战时考虑到了一个具体的应用：研究大气气溶胶--尘埃--云-辐射相互作用，以及气溶胶和尘埃如何影响云微物理，以及更广泛地说，影响气候动力学。该项目的成功完成要求在开发分布式数据融合算法方面取得进展，以帮助估计时空过程和利用这些环境过滤器的协调算法。由此产生的运动计划将用于对太平洋地区云层上的气溶胶和尘埃进行采样。在该项目框架内开发的新概念工具将使移动机器人网络得以进一步发展，其能力远远超过目前技术所提供的能力。算法和运动计划将在专门为监测大气过程而开发的多无人机系统中进行调整和测试。移动机器人车辆网络实时收集现场数据的可能性将极大地扩展远程测量和驱动的可能性。特别是，无人机组对大气数据的动态自适应收集将为人类活动如何影响气候变化提供亟需的洞察。

The main objective of this project is the development of scalable,systematic approaches to the synthesis of adaptive sampling strategiesby mobile sensor networks. In particular, the research challenges havebeen identified with a specific application in mind: the study ofatmospheric aerosol-dust-cloud-radiation interactions, and howaerosols and dust affect cloud microphysics and, more generally,climate dynamics. The successful completion of the project requiresprogress on the development of distributed data fusion algorithms thathelp estimate spatio-temporal processes and coordination algorithmsthat exploit these environmental filters. The resulting motion planswill be adapted for the sampling of aerosols and dust on clouds acrossareas of the Pacific Ocean.The novel conceptual tools developed in the framework of this projectwill allow the further development of mobile robotic networks withcapabilities that are well beyond those offered by the currenttechnology. Algorithms and motion plans will be adapted and tested ina multi-UAV system specially developed to monitor atmosphericprocesses. The possibility of having a network of mobile roboticvehicles collecting in-situ data in real time will greatly extend ourabilities for remote measurement and actuation. In particular, thedynamic adaptive gathering of atmospheric data by groups of UAVs willprovide a much needed insight into how human activity affects climatechange.