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Monitoring Atmospheric Phenomena within Low-Altitude Clouds! with a Fleet of Fixed-Wing UAVs

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HAL Id: hal-01232119

https://hal.archives-ouvertes.fr/hal-01232119

Submitted on 22 Nov 2015

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Monitoring Atmospheric Phenomena within

Low-Altitude Clouds! with a Fleet of Fixed-Wing UAVs

Alessandro Renzaglia, Christophe Reymann, Simon Lacroix

To cite this version:

Alessandro Renzaglia, Christophe Reymann, Simon Lacroix. Monitoring Atmospheric Phenomena

within Low-Altitude Clouds! with a Fleet of Fixed-Wing UAVs. Workshop on Principles of Multi-

Robot Systems, at Robotics: Science and Systems, Jul 2015, Roma, France. 2015. �hal-01232119�

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Monitoring Atmospheric Phenomena within Low-Altitude Clouds !

with a Fleet of Fixed-Wing UAVs !

Context

Alessandro Renzaglia, Christophe Reymann, Simon Lacroix

SkyScanner: 2 years long research project involving atmosphere scientists and drone / robotics scientists

•  Refine aerological models of cumulus clouds

•  Enduring agile drone conception and control

•  Fleet control www.laas.fr/projects/skyscanner

2-levels approach

•  High level mission planning (coarse macroscopic cloud model, drone allocation to given regions)

•  Local planning (dense cloud model, drone trajectories optimization)

Dense cloud mapping

Mapping a 4D structure from data perceived over a (small) set of 1D manifolds

•  Parameters to estimate: 3D winds, P, T, U, LWC…

•  Sparse information: use of Gaussian regression processes

Planning trajectories

Experiments

@ENAC

Challenges:

•  Optimize hyper parameters learning (exploit sparsity, develop incremental schemes, …)

•  Choice of the kernel

•  Exploit mapped parameters correlations

•  Relations with the coarse macroscopic model?

MesoNH cloud simulation Produced by Faycal Lamraoui

CNRM/GAME Laboratory, Toulouse

Maximizing the utility gathered along the path taking into account the air flows

•  Finite-horizon optimization problem

•  Planning in the control space composed of two phases:

1.  blind Random Search to initialize the trajectories 2.  constrained Simultaneous Perturbation Stochastic

Approximation algorithm to converge to a local maximum

Challenges:

•  Sound definition of the utility

•  Exploiting a realistic energetic model

•  Multi-criteria optimization scheme

•  Learn planning hyperparameters (δx, δt)

•  Aircraft modeling: aerodynamic and propulsion models, polar curve

•  On-line wind estimation

Vane to measure the angle of attack

Paparazzi

autopilot

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