N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 1/16 Adaptive Sampling and Prediction (ASAP) Additional Collaborators: Jim Bellingham (MBARI)

Slides:



Advertisements
Similar presentations
Future Directions and Initiatives in the Use of Remote Sensing for Water Quality.
Advertisements

N.E. Leonard – U. Pisa – April 2007 Slide 1 Cooperative Control and Mobile Sensor Networks Application to Mobile Sensor Networks, Part II Naomi Ehrich.
David Rosen Goals  Overview of some of the big ideas in autonomous systems  Theme: Dynamical and stochastic systems lie at the intersection of mathematics.
ROMS User Workshop, October 2, 2007, Los Angeles
Database Software Creation Process Arvin Meyer, MCP, MVP
Cloud Computing Resource provisioning Keke Chen. Outline  For Web applications statistical Learning and automatic control for datacenters  For data.
The Adaptive Sampling and Prediction (ASAP) Program A Multi-University Research Initiative (MURI) Learn how to deploy, direct, and utilize autonomous vehicles.
Attribution of Haze Phase 2 and Technical Support System Project Update AoH Meeting – San Francisco, CA September 14/15, 2005 Joe Adlhoch - Air Resource.
A discussion on.  Path Planning  Autonomous Underwater Vehicles  Adaptive Sampling  Mixed Integer Linear programming.
Kevin Deardorff Assistant Division Chief, Decennial Management Division U.S. Census Bureau 2014 SDC / CIC Conference April 2, Census Updates.
NPS Institutional Contributions Aircraft Overflights (RAMP, paduan, q.wang) –Plume Mapping (SST, color, visual) –Surface Heat Fluxes Expanded HF Radar.
Copyright MBARI 2006 Estimating Ocean Heat Flux Using AUVs: Error Analysis, Sampling Design, and Preliminary Results from the MB06 Experiment Yanwu Zhang.
N.E. Leonard – ASAP Progress Meeting – February 17-18, 2005 Slide 1/22 ASAP Progress Report Adaptive Sampling and Cooperative Control Naomi Ehrich Leonard.
Click to edit Master title style Click to edit Master text styles Second level Third level Fourth level Fifth level 1 david m fratantoni physical oceanography.
Real-Time ROMS Ensembles and adaptive sampling guidance during ASAP Sharanya J. Majumdar RSMAS/University of Miami Collaborators: Y. Chao, Z. Li, J. Farrara,
Adaptive Sampling And Prediction Dynamical Systems Methods for Adaptive Sampling ASAP Kickoff Meeting June 28, 2004 Shawn C. Shadden (PI: Jerrold Marsden)
Science Board ( Leonard, Kuperman, Schmidt, Howe, D’Spain, Greg, Van Holiday, Chavez ) Program Management (Curtin, Harper, Ekman, Traweek, Dietz) System.
CPSC 695 Future of GIS Marina L. Gavrilova. The future of GIS.
NRL modeling during ONR Monterey Bay 2006 experiment. Igor Shulman, Clark Rowley, Stephanie Anderson, John Kindle Naval Research Laboratory, SSC Sergio.
SIO ‘Spray’ Gliders for ASAP Goals Contribute to synoptic mapping & upwelling heat budget analysis Develop methods for array optimization & automatic assistance.
Systems Oceanography: Observing System Design. Why not hard-wire the system? Efficiency of interface management –Hard-wire when component number small,
PROSEMINAR Environmental Management ENVR E-200. DO YOU HAVE? System for keeping notes on your readings and draft writing Information on your primary target.
Adaptive Sampling RTOC 8/24/03 AS Goals/Metrics Review Expanded Dorado Experiment Prepared by Ralf Bachmayer, Pradeep Bhatta Princeton University.
1 ROMS (Regional Ocean Modeling System) Real-Time Modeling, Data Assimilation, and Forecast FY : ONR –AOSN Monterey Bay field experiment FY 2004:
ASAP Kickoff Meeting June 28-29, 2004 Adaptive Sampling Plans: Optimal Mobile Sensor Array Design Naomi Ehrich Leonard Mechanical and Aerospace Engineering.
Aeronautics & Astronautics Autonomous Flight Systems Laboratory All slides and material copyright of University of Washington Autonomous Flight Systems.
The Context of Forest Management & Economics, Modeling Fundamentals Lecture 1 (03/30/2015)
Sensor Coordination using Role- based Programming Steven Cheung NSF NeTS NOSS Informational Meeting October 18, 2005.
Final General Assembly – Paris, France – September 19, 2014 FP7-Infra : Design studies for European Research Infrastrutures 1st October 2011.
Slide Adaptive Sampling and Prediction (ASAP) AOSN-II Undersea Persistent Surveillance (UPS) Autonomous Wide Aperture.
CeNCOOS Glider Activities Francisco Chavez Senior Scientist Monterey Bay Aquarium Research Institute.
UNDERWATER GLIDERS.
N.E. Leonard – U. Pisa – April 2007 Slide 1 Cooperative Control and Mobile Sensor Networks Application to Mobile Sensor Networks, Part I Naomi Ehrich.
Dr. Frank Herr Ocean Battlespace Sensing S&T Department Head Dr. Scott L. Harper Program Officer Team Lead, 322AGP Dr. Martin O. Jeffries Program Officer.
Generic Approaches to Model Validation Presented at Growth Model User’s Group August 10, 2005 David K. Walters.
Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss.
Automated Weather Observations from Ships and Buoys: A Future Resource for Climatologists Shawn R. Smith Center for Ocean-Atmospheric Prediction Studies.
1 | Program Name or Ancillary Texteere.energy.gov Water Power Peer Review Assessment of Energy Production Potential from Ocean Currents along the United.
MURI: Integrated Fusion, Performance Prediction, and Sensor Management for Automatic Target Exploitation 1 Dynamic Sensor Resource Management for ATE MURI.
1 Distributed and Optimal Motion Planning for Multiple Mobile Robots Yi Guo and Lynne Parker Center for Engineering Science Advanced Research Computer.
Optimization & Control Optimal predetermined path — 1 stage of adaptivity  Network optimization algorithm  Non-linear programming Optimal adaptive sampling.
W.G. Leslie, P.J. Haley, Jr., Pierre F.J. Lermusiaux (PI), MIT Multidisciplinary Simulation, Estimation, and Assimilation Systems (MSEAS) OOI Cyberinfrastructure:
Wayne G. Leslie 13 November 2002 Harvard Ocean Prediction System (HOPS) Operational Forecasting and Adaptive Sampling.
N.E. Leonard – MBARI – August 1, 2006 Slide 1/46 Cooperative Control and Mobile Sensor Networks in the Ocean Naomi Ehrich Leonard Mechanical & Aerospace.
Frankfurt (Germany), 6-9 June 2011 SmartLife Guillaume & SmartLife Core Group – France – S1 – Paper SmartLife initiative in Focus.
OOI CI A&S (modeling) Yi Chao )
Student Name USN NO Guide Name H.O.D Name Name Of The College & Dept.
Sensing Hazards with Operational Unmanned Technology: NOAA's multi-year plan to deploy the NASA Global Hawk aircraft for high impact weather Michael L.
Unmanned Mobile Sensor Net - Ben Snively Unmanned Underwater Gliders Survey and extensions to work from: COOPERATIVE CONTROL OF COLLECTIVE MOTION FOR OCEAN.
Thrust IIB: Dynamic Task Allocation in Remote Multi-robot HRI Jon How (lead) Nick Roy MURI 8 Kickoff Meeting 2007.
1 A multi-scale three-dimensional variational data assimilation scheme Zhijin Li,, Yi Chao (JPL) James C. McWilliams (UCLA), Kayo Ide (UMD) The 8th International.
November 1, 2004 ElizabethGallas -- D0 Luminosity Db 1 D0 Luminosity Database: Checklist for Production Elizabeth Gallas Fermilab Computing Division /
Adaptive Sampling for MB’03 – Preliminary Update for Discussion Naomi Ehrich Leonard Mechanical and Aerospace Engineering Princeton University and the.
Multidisciplinary Ocean Dynamics and Engineering Laboratory: Simulation, Estimation and Assimilation Systems Massachusetts Institute of Technology, Department.
Future needs and plans for ocean observing in the Arctic AOOS Arctic Town Hall Futur Zdenka Willis Integrated Ocean Observing System National Program Office.
Ocean Observatories Initiative OOI CI Kick-Off Meeting Devils Thumb Ranch, Colorado September 9-11, 2009 Observation Planning and Autonomous Mission Execution.
University of Pennsylvania 1 GRASP Control of Multiple Autonomous Robot Systems Vijay Kumar Camillo Taylor Aveek Das Guilherme Pereira John Spletzer GRASP.
Enabling Team Supervisory Control for Teams of Unmanned Vehicles
CANAL NETWORK CONTROL AJAY K BASU
Modeling and data assimilation in Monterey Bay Area.
Coupled atmosphere-ocean simulation on hurricane forecast
System Control based Renewable Energy Resources in Smart Grid Consumer
Data Communications Infrastructure Discussed in Separate Document
System goal platform controller data model.
Glen Gawarkiewicz Andrey Shcherbina Frank Bahr Craig Marquette
October 27, 2011 New Brunswick, NJ
Priorities for Next Steps in the ASAP Research program
Modeling, Predictions and Adaptive Sampling Team
UNDERWATER GLIDERS.
SIO Contributions for ASAP 2006
Presentation transcript:

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 1/16 Adaptive Sampling and Prediction (ASAP) Additional Collaborators: Jim Bellingham (MBARI) Yi Chao (JPL) Sharan Majumdar (U. Miami) Igor Shulman (NRL, Stennis) Principal Investigators for MURI: Russ Davis (SIO) David Fratantoni (WHOI) Naomi Leonard (Princeton) Pierre Lermusiaux (Harvard) Jerrold Marsden (Caltech) Steven Ramp (NPS) Alan Robinson (Harvard) Henrik Schmidt (MIT) Planning Meeting October 6-7, 2005 Princeton University

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 2/16 ASAP Program Ocean Processes Ocean Model Model Prediction Data Assimilation Adaptive sampling Learn how to deploy, direct and utilize autonomous vehicles (and other mobile sensing platforms) most efficiently to sample the ocean, assimilate the data into numerical models in real or near-real time, and predict future conditions with minimal error.

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 3/16 Goals of ASAP Program 1.Demonstrate ability to provide adaptive sampling and evaluate benefits of adaptive sampling. Includes responding to a. changes in ocean dynamics b. model uncertainty/sensitivity c. changes in operations (e.g., a glider comes out of water) d. unanticipated challenges to sampling as desired (e.g., very strong currents) 2.Coordinate multiple assets to optimize sampling at the physical scales of interest. 3.Understand dynamics of 3D upwelling centers a.Focus on transitions, e.g., onset of upwelling, relaxation. b.Close the heat budget for a control volume with an eye on understanding the mixed layer dynamics in the upwelling center. c.Locate the temperature and salinity fronts and predict acoustic propagation.

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 4/16 Approach 1.Research that integrates components and develops interfaces. 2.Focus on automation and efficient computational aids to decision making. 3.Virtual pilot study in December 2005? Develop simulator. 4.Field experiment in August 2006.

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 5/16 Glider Plan Guidelines: A.The sampling plans should be designed for data collection that a.Gives data that is understandable (in the oceanographic context) without a model. b.Provides a good map regardless of model. c.Yields good field + parameter estimates when combined with dynamical model. B.Sampling plans should a.Be fully adaptive under operational constraints. b.Change in response to optimization of integration of coverage + sensitivity metrics. C.Gliders operated as cooperative fleet: adaptation of 1 glider implies adaptation of all. Optimal Coordinated Trajectory (OCT). A coordinated sampling pattern for the glider fleet that has been optimized with respect to the above objectives. Coordination. Prescription of the relative location of all gliders (as a function of time).

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 6/16 Glider Plan Feedback to large changes and disturbances. Gross re- planning and re-direction. Adjustment of performance metric. Feedback to switch to new OCT. Adaptation of collective motion pattern/behavior (to optimize metric + satisfy constraints). Feedback to maintain OCT. Feedback at individual level that yields coordinated pattern (stable and robust to flow + other disturbances). Many individual gliders. Other observations. Models and model products

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 7/16 Glider Plan OCT for increased sampling in southwest corner of ASAP box. Candidate default OCT with grid for glider tracks. Adaptation SIO glider WHOI glider

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 8/16 Glider Plan Voting System A web site will be created in which anyone from the team can propose a new area of the ASAP box for special sampling attention (ie propose a rectangle or rectangle from the candidate grid with corresponding time period of interest) and provide a justification for their proposal. There will be a vote on-line. Each group has a vote. Rules for voting TBD. Justification for strong endorsements and rejections of proposals critical. A minimum time interval between consecutive proposals will be imposed. The goal is to implement important adaptation proposals and to be sure to try all the different reasons for adaptation (dynamics, sensitivity, …) over the course of the experiment. Physically possibility will be checked (e.g., can the gliders get to location in timely fashion). Designated person will review all input and make final decision.

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 9/16 ASAP DATABASE & VISUALIZATION System Plan Observations: HF radar, NPS aircraft, satellite, wind HOPS, ROMS, ICON, OA ESSE, ETKF, LCS, … Feedback to large changes Feedback to switch to new OCT Feedback to maintain OCT Glider observations Gliders MIT AUVs MIT AUV observations Model output Observations ASAP team votes to initiate adaptation Glider waypoints (and plans) Request for adaptation Glider planner ASAP OBSERVATION DATA FLOW ASAP MODEL/PRODUCT DATA FLOW PUBLIC WWW ??

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 10/16 Virtual Pilot Study (VPS)

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 11/16 Virtual Pilot Study (VPS)

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 12/16 ASAP DATABASE & VISUALIZATION Virtual Pilot System Plan Simulated ? HF radar, NPS aircraft, satellite, wind HOPS, ROMS, ICON, OA ESSE, ETKF, LCS, … Feedback to large changes Feedback to switch to new OCT Feedback to maintain OCT Glider observations Simulated gliders Simulated ? AUVs MIT AUV observations Model output Observations ASAP team votes to initiate adaptation Glider waypoints Request for adaptation Glider planner ASAP OBSERVATION DATA FLOW ASAP MODEL/PRODUCT DATA FLOW PUBLIC WWW ?? Harvard Aug Ocean

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 13/16 Virtual Pilot Study (VPS) -- To be resolved Schedule for VPS? What period of Harvard Aug 2003 ocean should we use? How much of the system plan do we anticipate that we can test in the VPS? Are there developments not yet reflected in system plan that we need to add? Are there critical pieces not getting attention? Who will do these? Where do we need additional effort in integration of methodologies? Where are the interfaces not yet complete? How might preliminary results of OSSE’s help us hone our plan? What remains to do to make data flow? What are remaining open questions that we should consider as a team?

N.E. Leonard – ASAP Planning Meeting - Oct. 6-7, 2005 Slide 14/16 Goals of Meeting Discuss and resolve issues and questions for VPS. Address new ideas and developments for VPS. Finalize VPS plans. Assign remaining action items to be completed in advance of VPS. Determine action items to be initiated in parallel with VPS. Discuss and resolve issues related to ASAP ’06 logistics. Discuss and resolve issues related to larger MB ‘06 multi-project integration (Curtin).