We study the problem of monitoring the evolution of atmospheric variables within low-altitude cumulus clouds with a fleet of Unmanned Aerial Vehicles (UAVs). To tackle this challenge, two main problems can be identified: i) creating on-line maps of the relevant variables, based on sparse local measurements; ii) designing a planning algorithm which exploits the obtained map to generate trajectories that optimize the adaptive data sampling process, minimizing the uncertainty in the map, while steering the vehicles within the air flows to generate energetic-efficient flights. Our approach is based on Gaussian Processes (GP) for the mapping, combined with a stochastic optimization scheme for the trajectories generation. The system is tested in simulations carried out using a realistic three-dimensional current field. Results for a single UAV as well as for a fleet of multiple UAVs, sharing information to cooperatively achieve the mission, are provided.

This paper presents an approach to guide a fleet of Unmanned Aerial Vehicles to actively gather data in low-altitude cumulus clouds with the aim of mapping atmospheric variables. Building on-line maps based on very sparse local measurements is the first challenge to overcome, for which an approach based on Gaussian Processes is proposed. A particular attention is given to the on-line hyperparameters optimization, since atmospheric phenomena are strongly dynamical processes. The obtained local map is then exploited by a trajectory planner based on a stochastic optimization algorithm. The goal is to generate feasible trajectories which exploit air flows to perform energyefficient flights, while maximizing the information collected along the mission. The system is then tested in simulations carried out using realistic models of cumulus clouds and of the UAVs flight dynamics. Results on mapping achieved by multiple UAVs and an extensive analysis on the evolution of Gaussian Processes hyperparameters is proposed.