Where to Drop Sensors from Aerial Robots to Monitor a Surface-Level Phenomenon?

We consider the problem of routing a team of energy-constrained Unmanned Aerial Vehicles (UAVs) to deploy sensors for monitoring a surface-level phenomenon in the presence of stochastic wind disturbances. Most of the environmental monitoring work considers the case where the sensors and their carrier are on one platform, and the sensing location can be controlled by controlling the carrier vehicle's position. In contrast, airdropping the sensors onto the ground can intro-duce stochasticity in the sensors' landing (i.e., measurement) locations. We focus on carefully choosing where to drop the sensors so that despite the measurement locations' stochasticity, we can still effectively monitor the surface-level phenomenon. Specifically, we introduce this problem where a team of UAVs must drop sensors within an allotted time budget. The objective is to maximize the mutual information between the phenomenon at Points of Interest and the measurements obtained at the random sensing locations. This is a variant of the Submodular Team Orienteering Problem with an additional constraint on the number of sensors each UAV can carry. We show that the stochasticity in the measurement location makes objective computationally expensive to compute. We propose a surrogate objective with a closed-form expression and an algorithm for the routing problem. We validate the proposed approach through extensive simulations using synthetic and a real-world dataset of Chlorophyll density from a Pacific Ocean sub-region.