1. Ocean Observatories Initiative OOI CI Kick-Off Meeting Devils Thumb Ranch, Colorado September 9-11, 2009 Observation Planning and Autonomous Mission Execution Steve Chien, David R. Thompson Jet Propulsion Laboratory, California Institute of Technology Copyright 2009 California Institute of Technology. Government Sponsorship Acknowledged.

2. Put sensors at the right place, at the right time Address changing science goals Integrate multiple data sources Respect known, predicted constraints Survive extreme environments, return home! OOI CI Kick-Off Meeting, Sept 9-11, 20092 Observation Planning Challenges AUV (courtesy Bluefin) CODAR Glider (Rutgers) Models (ESPRESSO) Hyperion

3. CI Scenario: feature tracking OOI CI Kick-Off Meeting, Sept 9-11, 20093 GPS Fix Comm Uplink 1 2 3 4 5 n... Glider path Dive Period Dive Depth GPS Fix Known constraints (Battery, shipping lanes) Uncertain constraints (time-varying currents) Must operate autonomously ~6-8 hours Rutgers University Follow a time-varying feature (shelf-slope salinity intrusion) Reconfigure a glider network “on the fly” Highly dynamic environment

4. OOI CI Kick-Off Meeting, Sept 9-11, 20094 Planning and Execution: different roles for diverse strengths Shore automation Onboard autonomy Integrate multiple data sources easily Balance subtle and changing goals Visualize data products Place high-level waypoints Optimize well-defined subtasks Good with tedious, quantitative details Optimize for currents Manage resource constraints Limited computer power Can immediately react to changing conditions Monitor state Adapt behaviors Run conditional plans Human planners

5. Detailed Plan Detailed Plan Uplink Cartographic Plan Cartographic Plan 2-phase mission planning OOI CI Kick-Off Meeting, Sept 9-11, 20095 Automatic path planning with ROMS predictions Create or modify waypoints Examine path, “reachability envelopes” Edit plan in timeline view Simulate adaptive behaviors & glider dynamics Schedule glider activities

6. start reachable unreachable glider path OOI CI Kick-Off Meeting, Sept 9-11, 20096 Current-sensitive plans Salinity intrusion Path planning in 3D spatiotemporal grid Exploits ROMS forecasts Compute reachability, travel times Salinity intrusion start glider path

7. OOI CI Kick-Off Meeting, Sept 9-11, 20097 Example: current-sensitive plans Fraction of random trials with successful paths 0-5 km5-10 km> 10 km Predictive53.3%50.7%46.0% Greedy33.7%33.1%26.1%

8. OOI CI Kick-Off Meeting, Sept 9-11, 20098 Cartographic interface Automates current-sensitive planning Users can focus on the science Reachability by hour

9. ASPEN Planner / Scheduler Tune flight parameters Manage resources Coordinate assets Schedule activities OOI CI Kick-Off Meeting, Sept 9-11, 20099 ASPEN Timeline user interface Model elements Initialize activity (start, duration) Location Communicate activity (start, duration) Location Goto waypoint activity (start, duration) Destination Fallback destination Energy required Flight parameters (depth, pitch) Loiter activity (start, duration) Sample activity (start, duration) Failsafe activity (start, duration) Vehicle health Payload health Battery energy

10. Fallback locations for timed sequences Prevents backtracking, orbiting, etc. Adds robustness to execution error Doesn’t require new current estimates MOOS-IvP adaptive behaviors Adaptive behaviors and conditional execution OOI CI Kick-Off Meeting, Sept 9-11, 200910 1km margin Nominal path Conditional path Salinity Intrusion MOOS-IvP

11. CI Planning and Execution Challenges OOI CI Kick-Off Meeting, Sept 9-11, 200911 Dynamic ocean is an exciting domain No bright line between observation planning and data distribution OOI CI network enables new planning strategies –Flexible task allocation –Automation on vehicle and shore

12. OOI CI Kick-Off Meeting, Sept 9-11, 200912 Thanks !

13. OOI CI Kick-Off Meeting, Sept 9-11, 200913 Backup Slides

14. Quantitative benefits OOI CI Kick-Off Meeting, Sept 9-11, 200914 0-5 km5-10 km> 10 km Predictive53.3%50.7%46.0% Greedy33.7%33.1%26.1% Fraction of random trials with successful paths Compared predictive planning vs. naïve alternative Significant improvements in number of valid paths, arrival time