CAMS

h1. CAMS — _Cloud Adaptive-Mapping System_

!{width:128px;float:right}https://redmine.laas.fr/attachments/download/3004/cams%20logo.svg! CAMS _(Cloud Adaptive-Mapping System)_ is a software framework for atmosphere scientists to carry out cloud observation missions using fleets of UAVs. This system provides a graphical user interface to create and manage flight plans adapted to cloud exploration, real-time mapping from cloud sensors to help operators make decisions about the exploration process and post-mission data analysis. CAMS is ready to work in mixed-reality scenarios combining physical and simulated UAVs with real or synthetic cloud environments. Additionally, UAV flight trajectories and sensor data are stored in a database during mission execution for further review and analysis.

CAMS has a Application Programming Interface _(API)_ for developers and cloud researchers to analyze collected data using the current cloud mapping algorithms and extend the system with new functions.

h2. Table of Contents

Operator's guide (Basic):

* [[System architecture]]:

** [[Principal functions]]

** [[Software components]]

** [[Modes of operation]] (Pure virtual, mixed-reality, field deployment and mission replay).

* User guide:

** [[Installation]]

** [[Running CAMS Graphical User Interface]]

** [[Scenario configuration]]

Developer's guide (Advanced):

* [[Detailed system description]]

* [[Communication framework]]?

* Getting started:

** [[Installation]]

** [[Running CAMS Graphical User Interface]]

** [[Scenario configuration]]

* Cookbook:

** [[Basic types]]

** [[Reading and processing information from a database]]

** [[Defining new aircraft using plugins]]

** [[Cloud mapping using Gaussian Process Regression]]

** [[Description of the MesoNH file format]]

** [[Paparazzi interface]]

* [[API reference]] (Class structure)

h2. About the Nephelae project

Atmospheric models still suffer from a gap between ground-based and satellite measurements. As a consequence, the impacts of clouds on our climate remain one of the largest uncertainties in numerical weather prediction (NWP) and in understanding climate change. The main goal of the Nephelae project is to fill that gap by acquiring micro-physical atmospheric measures inside clouds by the means of a fleet of Unmanned Arial Vehicles (UAVs) simultaneously flying inside a target cloud. More information about the project's vision can be found on the "ANR website":https://anr.fr/fr/projets-finances-et-impact/projets-finances/projet/funded/project/anr-17-ce01-0003/?tx_anrprojects_funded%5Bcontroller%5D=Funded&cHash=ef4a5ab7a633cb40b9174c32ef4a926d.

h3. Partners

* *CNRM* _(Project Coordinator)_: Provider of expertise on atmospheric phenomenons and exploitation of UAVs for atmospheric measurements. They also provide the sensors embedded into the UAVs measuring cloud properties. "More information about the operations carried out by this partner":http://www.umr-cnrm.fr/spip.php?rubrique266&lang=fr.

* *ENAC*: Design and manufacturing of a new UAV model built specifically for cloud exploration. Operation of UAV fleets with the "Paparazzi framework":http://paparazziuav.org, which includes an on-board autopilot software and a ground control software built for operator to supervise the fleet.

* *LAAS*: Design and programming of a real-time cloud mapping system that aims to provide operators with a graphical feedback of the cloud mapping process, maps of the observed clouds and  and the current state of fleet operations. The user interface also accepts operator inputs to create and adapt UAV flight plans in order to maximize data acquisition of target clouds. Currently mission planning is manual but in the future this process will be autonomous guided by user requirements.