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MERS

MERS. MERS. Unifying Model-based Programming and Agile Path Planning Through Optimal Strategy Search. By: Aisha Walcott MERS (Model-based Embedded and Robotic Systems) Space Systems + AI Labs Research Advisor: Brian Williams. Motivation. UAVs provide food and supplies in hostile areas

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MERS

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  1. MERS MERS Unifying Model-based Programming and Agile Path Planning Through Optimal Strategy Search By: Aisha Walcott MERS (Model-based Embedded and Robotic Systems) Space Systems + AI Labs Research Advisor: Brian Williams

  2. Motivation • UAVs provide food and supplies in hostile areas • Urban search and rescues • Mars exploration Autonomous Vehicles

  3. Outline • Current Work • Research Objectives • Technical Approach • Conclusions

  4. Current Work Model-based Temporal Planning System(Kirk) RMPL Kirk Planner / Scheduler optimal search Activity plan model path planning RMPL program Schedulable / consistent plan re-plan request Compiler Plan Runner HCA Model Converter Fast planning Scheduling TPN graph • RMPL (Reactive Model-based Programming Language) • Specifying team strategies and contingencies • Concurrent/group behavior • Choices/contingencies • Timing constraints • Maintenance conditions commands status update Robot Interface Autonomous Vehicles [MERS, 1999]

  5. Current Work (cont’d) • Ex: RMPLTemporal Planning Network (TPN) RMPL (Take_Picture ( ) [25,35] (parallel (camera = on) (sequence ( ( Advance_Film ( ) ) [7,15] ) ( ( Snapshot ( ) ) [3,6] ) ) ;;end sequence ) ;; end parallel );;end activity Take Pictures( )[25,35] Advance_Film() [7,15] Snapshot() [3,6] Tell(camera = on)

  6. Scenario • Search Building Example (Story+characs) • Objective search the building • Get into the building

  7. Research Objectives • Leverage model-based programming to encode strategies that involve activities constrained by location • Unify path planning and activity planning • Activities and paths effect the plan • Incorporate costs (i.e. activity costs) • Design a globally optimal planning system • Employ a best-first plan search strategy

  8. Technical Approach • Kinodynamic path planning • Rapidly-exploring random trees (RRTs) [La Valle and Kuffner, 1999] • Agile maneuvers [Frazzoli et. al., 2001] • Merge model-based programming and roadmap-based path planning • Unified planning model-RRTTPN • RMPL location constraints • Optimal plan search

  9. Strategy Selection RMPL Mission Strategies Global Path Planning Robot Models Kinodynamic Maneuver Planning System Overview RMPL Control Program (USAR strategies) TPN Robot Model Optimal Search RRTPN RRT Path Planner

  10. RMPL to TPN • Do maintaining Search_Building() [ ] 5 G (ANW1.TakePictures())[ ] S 5 35 J K (ANW1= window1) [ ] 5 5 5 B D 5 5 (ANW1 = cafeteria)[ ] R 5 H H H A F 5 L M 5 5 5 5 5 C E I Q 5 5 (ANW1 = window2)[ ] N P

  11. Grammar • Model-based programming with RMPL, for programs with location constraints • Achieve constraints

  12. Example Opty Search

  13. RRT Construction

  14. m1 m2 m3 m4 m5 m6 Kinodynamic Path Planning Nodes: States Edges: Control Inputs Goal: sgoal Si control inputs u Si+1 Agile Maneuvers sinitial Maneuver Automaton A Maneuver M consisting of a sequence of maneuvers, m1..mn, that drive the system from start to goal sfinal Si What is the best trajectory to follow from Si to Si+1? Maneuver Library (pre-computed) Start:sstart Si+1 Rapidly-exploring Random Trees RRTs

  15. Activities and Paths RRT Temporal Plan Network TPN RRT Merging Model-based Programming and Agile Path Planning Activities Paths Plan space TPN RRT State space RRTTPN Planning Model Location constraints Memory-bounded optimal plan search

  16. Contributions • RRTPN • Optimal strategy selection

  17. RRT (start) (goal) RRTTPN • Locations in RMPL/TPN can grow RRTs in effort to satisfy the location constraints Minimum and Maximum time to get to Location C. That is, it takes at least 95 units of time to get to location C and at most 105 units of time to get to location C Signal that Robot cannot be going to any other location during this time interval [95,105] Tell(Robot.location = C) total time = 100 Tell(Not(Robot.location = C)) [95,105] If…Path Found -Return total time to travel from start to goal -Return cost of path If…No Path Found -Add a Tell( NOT( Blimp = location)) constraint -Or if total time exceeds bound RRTTPN planning model Location constraints Memory-bounded optimal plan search

  18. RRTTPN Conversion of RRT generated controls and waypoints to TPN activities Mapping from RRT to TPN Tell(Robot.location = A) Activity: Apply_Controls( control inputs ) [L,U] RRTTPN planning model Location constraints Memory-bounded optimal plan search

  19. Conclusions • RMPL programs more powerful and expressive with location constraints • Quickly identify activities that are not possible (robot can not get to the required location) • Memory-bounded search enables efficient use of computing resources • Save memory to store data, images, sounds, etc. • Save space for on-the-fly path planning • Globally optimal plan • Provide estimated activity costs with RMPL program • Improves the likelihood of successful plan execution

  20. The End • Thank you

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