The main objective of this project is the development of scalable, systematic approaches to the synthesis of adaptive sampling strategies by mobile sensor networks. In particular, the research challenges have been identified with a specific application in mind: the study of atmospheric aerosol-dust-cloud-radiation interactions, and how aerosols and dust affect cloud microphysics and, more generally, climate dynamics. The successful completion of the project requires progress on the development of distributed data fusion algorithms that help estimate spatio-temporal processes and coordination algorithms that exploit these environmental filters. The resulting motion plans will be adapted for the sampling of aerosols and dust on clouds across areas of the Pacific Ocean.
The novel conceptual tools developed in the framework of this project will allow the further development of mobile robotic networks with capabilities that are well beyond those offered by the current technology. Algorithms and motion plans will be adapted and tested in a multi-UAV system specially developed to monitor atmospheric processes. The possibility of having a network of mobile robotic vehicles collecting in-situ data in real time will greatly extend our abilities for remote measurement and actuation. In particular, the dynamic adaptive gathering of atmospheric data by groups of UAVs will provide a much needed insight into how human activity affects climate change.
