Wireless Sensor Networks (WSNs) are being envisioned to monitor health and vitals of the nation?s critical infrastructure, e.g. bridges, roads, and buildings. Other applications include monitoring remote space stations and off-earth structures, e.g. the inflatable lunar habitats. A significant challenge in making remote monitoring a reality is data collection from the sensors, as their batteries do not allow long-range transmission. This research enables data collection and inspection via Unmanned Aerial Vehicles (UAVs), invoking subsequent investigation in dynamic task allocation, distributed path planning, and collaborative navigation in GPS-denied/indoor settings. In particular, this study develops the theoretical foundations for autonomous monitoring and inspection, where WSNs operate in a complete harmony with a network of UAVs. The WSN provides intelligible information to the UAVs for responsive and diagnostic actions; while the UAVs coordinate (and may also bring outside directives) to adapt the WSN to the environment and application demands.
To undertake this effort, this research casts the predominantly nonlinear UAV control, navigation, and planning problems in a completely linear framework. The linearity of the underlying dynamics builds upon some of the classical work in convex geometry and Euclidean metrics. Subsequently, the distributed control problem is formulated using structured systems theory, by which efficient graph-theoretic controllability and actuation methods are developed. These features play a significant role in establishing rigorous analytical arguments and in implementing a fully functional remote monitoring and inspection prototype. The transformative approach adapts to the application needs and is robust to imperfections in the underlying communication and environment uncertainties. The study further involves several outreach efforts via social coding platforms and workshops for minority, women in engineering, and K-12 students.
