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Army Research Laboratory

Army Research Laboratory. A Weather Decision Aid for Unmanned Aircraft Missions. David Knapp Edward Measure David Sauter Terry Jameson US Army Research Laboratory Battlefield Environment Division White Sands Missile Range, NM DSN 258-4574, Commercial 505-678-4574 dknapp@arl.army.mil

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Army Research Laboratory

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  1. Army Research Laboratory A Weather Decision Aid for Unmanned Aircraft Missions David Knapp Edward Measure David Sauter Terry Jameson US Army Research Laboratory Battlefield Environment Division White Sands Missile Range, NM DSN 258-4574, Commercial 505-678-4574 dknapp@arl.army.mil Presented at the National Weather Association Annual Meeting 19 Oct 2006

  2. ARMY RESEARCH LABORATORY OUTLINE Research background and goals R&D and Operational Concepts Accomplishments and Progress Milestones and Summary 2

  3. From UAS Roadmap, 2005-2030Office of the Secretary of Defense, Aug05 • Goals for unmanned aviation (applicable to civilian aircraft as well): • #7: Improve adverse-weather Unmanned Aircraft System (UAS) capabilities to provide higher mission availability and mission effectiveness rates • RECOMMENDATION: • Incorporate and/or develop all-weather practices into future UAS designs All-weather “practices” can only be implemented when you KNOW what the current and predicted weather is/will be along the flight path. THIS is the approach Army Research Lab is taking. 3

  4. WHERE DO WE WANT TO GO? • Development of decision aid technology to incorporate tailored weather support to UAS flights. • GOAL: Improve UAS Mission Success Rates! • Integrate weather impacts with UAS mission profiles • Depict weather impacts along the mission route • Determine optimal flight path; avoiding unfavorable weather conditions • M2M Capabilities INTEL Analysis Operator Tasking Sensor Collection Sensor Payload Selection Platform Protection Weather Support to the UAS Mission From Pre-launch to Post-recovery Forecast & Effects Decision Aids En Route Weather Updates Mission Planning & Execution 4

  5. GENERAL SUPPORT CONCEPT Regional-scale forecast model database… “Weather Data Cube”  4-D gridded fields of weather parameters UAS Weather Decision Aid Products for manual and automated applications. Nesting  Nowcast 0-3hr Database. Automated refresh of forecast 4-D cube for pre-mission & enroute updates. LOCAL SENSORS Surface Data Sensors Upper-Air Sensors UAS MET Sensors 5

  6. The R&D Concept • Combine 4-D UAS path with 4-D weather effects cube. Calculate weather adverse impacts on flight path. • Visualize impacts along UAS path. • Develop/implement route optimization scheme for determination of “best” course given user constraints and forecast weather. • Weather INtelligence – Routing = WIN-R • Cheesy Slogan Development: Don’t use environmental Line Of Sight-Routing, use Weather Intel-Routing • Translation: “Fly with a WIN-R, not a LOS-R” 6

  7. 4-D Weather Impacts Grid 4-D Weather Impacts Grid New 4-D Weather Forecast Grid Aircraft-Specific Weather Impacts Threshold Rules Integrating Weather Impacts Into Mission Profiles + = = + Initial/Current Flight Path WIN-R: New Flt Path Options; Avoiding Enrte Hazards Altered Flt Path (if needed) 7

  8. “Optimized” Flight Path Weather INtelligence – Routing = WIN-R 8

  9. “Optimized” Flight Path Weather INtelligence – Routing = WIN-R • Original planned flight path routes through “red” or “unfavorable” conditions • Automated flight route optimization algorithms to provide alternate routes around, over, under unfavorable conditions • Look for the “greenest” or “most favorable” path • Solution is an “all-weather” routing option to increase • mission success rates. • Technology applicable to ALL aircraft Target Area 2 T=6hr FL100 Target Area 1 T=3hr FL140 FL090 Target Area 3 T=9hr FL080 Alternate/ Optimized Route Optimized FL050 = GREEN Planned route FL040 FL060 Takeoff, T=0hr FL040 Landing, T=12hr 9

  10. Current Demo Work TDA Testbed co-located with UAS Technical Analysis and Applications Center (TAAC) • Initial Decision Aid Support: • 5km MM5 grids • Stand-alone UAS rules-driven decision aid • Acoustic Detection TDA tailored to UAS ops • DoD & Civilian weather web page products • Test periods started late FY05 TAAC Area Of Interest (AOI) • Future work: • Real-time weather obs assimilation from all sources (incl. on-board TAMDAR) • Local 3-hr Nowcasts updating weather database and “correcting” local forecast grids in real time • WIN-R capability - Customized, tailored, and automated flight route optimization for weather hazards avoidance. • Decision Aid available with user access to model weather data “cube” • Commercial Joint Mapping Tool Kit (C-JMTK) & FalconView compatible data, displays, & visualizations • M2M Capabilities 10

  11. 3D Flight Path Capability 11

  12. WIN-R Development: Adapting the “A*” Routing Algorithm • Computes the “Lowest Cost” path between points. • Cost function can represent fuel consumption, hazard to the aircraft, mission constraints, etc. • Searches out from a starting point, storing partial paths after each step. • The partial paths are stored in a prioritized list. • A Cost Function then determines the “Lowest Cost” of the paths in the priority list. • A* is guaranteed to find a lowest cost path if it exists, and is usually computationally cheaper than an exhaustive (or breadth first) search. 12

  13. A* on a 2-D Grid The numbered squares are the searched squares with the costs to reach them. The yellow path is the “least cost” path. 13

  14. A A A B B B Worst conditions shown for all altitudes Planned route from A to B at FL100 passes through unfavorable conditions Worst Conditions at FL100 (+/- 500’) 100 080 080 090 Optimized route at varying FLs Applying A* to Flight Route Optimization 14

  15. I M I M I M I M I M TECHNOLOGY IMPLEMENTATION TIMELINE FY06 FY07 FY08 FY09 FY10 Integrate MET & Mission Profiles Airframe & sensor thresholds for best flight path WIN-R optimal flight routing M2M routing capabilities Technology Integration Into Command & Control Systems & Processes I = Initial Capability M = Mature Capability 15

  16. US Army Corps of Engineers® Engineer Research and Development Center SUMMARY • Tailored unmanned aircraft weather support from planning to launch to landing/recovery -- Technology applicable across aviation spectrum – Moving towards M2M capabilities • Initial capability development and testing underway. Incremental improvements towards operational capabilities FY07+ • Technology Transition to DoD weather forecasters and mission planners, unmanned aircraft pilots, etc. Stand-alone capabilities planned. Non-DoD civilian applications??? • Endorsements of support from across the DoD aviation spectrum. 16

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