SkyScanner. Deploying fleets of enduring drones to probe atmospheric phenomena " Kicked off on June the 16 th, 2014
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1 SkyScanner Deploying fleets of enduring drones to probe atmospheric phenomena " Kicked off on June the 16 th, 2014 Project supported by the STAE foundation, 2014 / 2016
2 Scope of the project" Overall target: follow the evolution of a cloud with multiple drones to study entrainment and the onset of precipitation" Characterize state of boundary layer below and surrounding a cloud " atmospheric stability" lifting condensation level " cloud updraft" Follow 4D evolution of the cloud " entrainment at edge" liquid water" cloud microphysical properties" Impacts the drone conception " and the fleet control
3 CNRM " Centre National de Recherches Météorologiques " Experts in atmospheric sciences, fly drones" Involved Partners" ISAE" Institut Supérieur de lʼaéronautique et de lʼespace " Experts in fluid dynamics, flight mechanics & drones" ONERA" The french aerospace lab " Experts in flight control" ENAC" École Nationale de lʼaviation Civile " Experts in drones (cf Paparazzi autopilot)" LAAS" Laboratory for Analysis and Architecture of Systems" Roboticists"
4 Scope of the project" 3 research axes:" Refine aerologic models of clouds" Conceive enduring micro-drones" Fleet control" Plus: experimental developments and validations "
5 The partners" "&'(%)%*%"+,-.-/'0% 1-2+.(% "&'(%:%*%;.++9% 0-49,-.%!"# $% "&'(%3%*%#425,'4/%2,-4+% %842%0-49,-.% Funding amounts to five 18 months postdocs / research engineers
6 Beyond SkyScanner" Numerous other dynamic atmospheric phenomena to analyze" Very similar problems in ocean sciences (e.g. mapping flows, plumes )" Numerous researches related to the conception and control of (micro)-drones (e.g. 24/7 flight, innovative platforms and actuators )" Numerous researches in fleets of aerial / ground / marine autonomous robots" "
7 Outline" 3 research axes:" Refine aerologic models of clouds" The scientific challenge" On the interest of drones" Conceive enduring micro-drones" Fleet control" Plus: experimental developments and validations "
8 Different sources and transport (i.e., volcanic eruptions, biomass burning, industrial pollution, dust)" Airborne measurements" Aerosol-cloud interactions" In-situ validation of numerical models, satellite data" <16-,9849%9-%0=8,809+,'>+%891-(6=+,'0%
9 Cloud microphysics" Entrainment is the mixing of saturated and unsaturated air into a cloud. Entrainment coefficient among most sensitive variables causing uncertainty in climate models. Homogeneous mixing:! evaporation >! mixing! evaporation of all cloud drops, but no change in droplet number! narrow cloud droplet spectrum Inhomogeneous mixing:! evaporation <! mixing! evaporation of cloud drops within entrained area reduces the total droplet number! no change in cloud droplet spectrum
10 Large Eddy simulations (LES)" Use of Meso-NH model Existing set of dry and cloudy convective boundary layer case studies Select cases and perform higher resolution simulations (10m) with release of UAS to 1. provide measured parameters 2. determine the flight strategy
11 Simultaneous observations with UAS" aerosol-cloud interactions & cloud microphysics atmospheric heating of aerosol layers
12 UAS Cost Comparison"
13 Requirements and constrains on drones" 4D sampling : 10m resolution within ~ a km 3, the longer the better (1/2 hour minimum)" Dynamic and local phenomena (need for maneuverability)" Dedicated instruments: PTU, 3D winds, droplets counter, radiation, aerosols " Need to optimize the drones conception, control and coordination"
14 Which is the best platform?"
15 Outline" 3 research axes:" Refine aerologic models of clouds" Conceive enduring micro-drones" Conception issues" Flight control, energy management" Instrumentation" Fleet control" Plus: experimental developments and validations "
16 From user needs to specifications" Optimize numerous trade-offs, considering:" 1. Payload requirements (temporal and spatial resolution)" 2. Mission profiles (duration, characteristic velocities, )" 3. Weight / energy management" 4. Realistic manufacturing constraints" Main trade-off for SkyScanner: endurance vs. maneuverability " Calls for system modeling / validation and optimization strategies"
17 Energy management" Exploiting thermals" Thermal detection, optimizing the trajectory w.r.t. altitude gain and atmospheric measures Harvesting gusts / shears" Dynamic soaring Use of LES flow fields to specify flight strategies and deduce all-round designs (on-going ISAE / UWE)
18 (Parenthesis: dynamic soaring)"
19 (Parenthesis: dynamic soaring)" On-going ISAE / UWE (V. Bonnin)"
20 (Parenthesis: dynamic soaring)" On-going ISAE / UWE (V. Bonnin)"
21 Dedicated instrumentation" Aerosol sampling (150 g) Cloud droplets (1.4 kg) Electrical field (<30 g) Particle size & number (580 g) Smoke aerosol (280 g) CCN aerosol (1.9 kg) Total particle number (870 g) Radiometers (<2 g) Broadband flux (190 g) Sun energy: visible (45 g) Ozone (600 g) Turbulence (100 g) Temperature / humidity / airspeed (<20 g)
22 Optimize the instrumentation" PTU Turbulences " (3D pitot tube, 190 g)" Broadband flux (190 g)" Sun energy (visible, 45 g)" Fulfill both scientific and guidance tasks:" Scientific targets: PTU, 3D winds, droplets counter, radiation, aerosols " Flight control: reactivity to environment (lift detection, gusts) and optimization of flight path (scientific target and energy management) " Stringent compactness and weight constraints (overall mass < 2.0 kg)"
23 Outline" 3 research axes:" Refine aerologic models of clouds" Conceive enduring micro-drones" Fleet control" Problem statement" Foreseen solutions" Plus: experimental developments and validations "
24 An adaptive sampling problem" Fleet control" Servo on the gathered information to gain more information" Optimize the drones trajectories (trade-off: explore vs. sustain)" Inputs" Models of the drones " Model of the cloud (initiated by LES, continuously updated on-flight)"
25 Analogy with multi-robot exploration" A well studied problem" Unknown " areas Mapped" areas Frontier Frontier-based exploration:" Who goes where?" How?"
26 A dynamic phenomenon " observed locally" Limits of the analogy " Air truth
27 A dynamic phenomenon " observed locally" Limits of the analogy " Known information at time t! 1. Where to gather new information?" 2. Who is flying where?"
28 Limits of the analogy " A dynamic phenomenon " observed locally"?? Known information at time t! 1. Where to gather new information?" 2. Who is flying where? And how? " Numerous issues to tackle Predefined patterns vs. pure adaptive solutions? Distributed vs. centralized solutions? Planning vs. optimizing? Waypoint nav. vs. trajectory following? Heterogeneous fleet?
29 Limits of the analogy " Predefined patterns vs. pure adaptive solutions?" Distributed vs. centralized solutions?" What information to exchange?" Planning vs. optimizing?" Waypoint nav. vs. trajectory following?" Heterogeneous fleet?"
30 Outline" 3 research axes:" Refine aerologic models of clouds" Conceive enduring micro-drones" Conception issues" Flight control" Fleet control" Plus: experimental developments and validations "
31 Commercially available airplane models" < 2.0 kg" Controlled by Paparazzi autopilot" Platforms"
32 Recent experiments (CNRM & ENAC)" Typical flight patterns and data" Profile to 3000 m.asl (spiral w/ 700 m Ø) Ascent / descent ratio (100 / -230 m.min -1 ) Boundary layer
33 Recent experiments (CNRM & ENAC)" Qualifying the instruments" GPS altitude (m asl) UAS exhaust 0 1k 10k Particle number (cm -3 ) Long time lag of mini-temperature probes
34 Recent experiments (CNRM & ENAC)"
35 Outline" 3 research axes:" Refine aerologic models of clouds" Conceive enduring micro-drones" Fleet control" Plus: experimental developments and validations "
36 Summary" 3 research axes:" Refine aerologic models of clouds" Conceive enduring micro-drones" Fleet control" Plus: experimental developments and validations "
37 Future work" 3 research axes:" Refine aerologic models of clouds" Conceive enduring micro-drones" Fleet control" Plus: experimental developments and validations "
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