Autonomous Robots

, Volume 42, Issue 2, pp 491–512 | Cite as

Adaptive sampling of cumulus clouds with UAVs

  • Christophe Reymann
  • Alessandro Renzaglia
  • Fayçal Lamraoui
  • Murat Bronz
  • Simon Lacroix
Part of the following topical collections:
  1. Active Perception


This paper presents an approach to guide a fleet of Unmanned Aerial Vehicles (UAVs) 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 energy-efficient 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.


Mapping with UAVs Active data gathering Path planning 



This work is made in the context of the SkyScanner project, supported by the STAE foundation.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.LAAS-CNRS, INSAUniversité de Toulouse, CNRSToulouseFrance
  2. 2.Météo-France/CNRS, CNRM/GAMEToulouseFrance
  3. 3.ENACToulouseFrance

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