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Executing Reactive, Model-based Programs through Graph-based Temporal Planning Phil Kim and Brian C. Williams, Artificial Intelligence and Space Systems.

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Presentation on theme: "Executing Reactive, Model-based Programs through Graph-based Temporal Planning Phil Kim and Brian C. Williams, Artificial Intelligence and Space Systems."— Presentation transcript:

1 Executing Reactive, Model-based Programs through Graph-based Temporal Planning Phil Kim and Brian C. Williams, Artificial Intelligence and Space Systems Labs Massachusetts Institute of Technology Mark Abramson Draper Labs

2 Outline Cooperative Vehicle Missions Model-based Programming Reactive Model-based Programming Language (RMPL) Temporal Plan Networks (TPN) Activity Planning (Kirk) Optional: Hybrid Activity/Path Planning

3 Cooperative Mars Exploration How do we coordinate heterogeneous teams of orbiters, rovers and air vehicles to perform globally optimal science exploration?

4 MIT Cooperative Vehicle Testbed Distributed Satellites: Spheres, Spheres, TechSat21

5 MIT Cooperative Vehicle Testbed Distributed Satellites: Spheres, Spheres, TechSat21 Aerobots: Indoor blimps

6 MIT Cooperative Vehicle Testbed Distributed Satellites: Spheres, Spheres, TechSat21 Aerobots: Indoor blimps Sensing: distributed, wireless sensor net

7 MIT Cooperative Vehicle Testbed Distributed Satellites: Spheres, Spheres, TechSat21 Aerobots: Indoor blimps Sensing: distributed, wireless sensor net Rovers: 1 ATRV Sr., 3 ATRV Jr

8 Outline Cooperative Vehicle Missions Model-based Programming Reactive Model-based Programming Language (RMPL) Temporal Plan Networks (TPN) Activity Planning (Kirk) Optional: Hybrid Activity/Path Planning

9 Why Model-based Programming? Create Embedded Languages That Reason from Commonsense Models Leading Diagnosis: Legs deployed during descent. Noise spike on leg sensors latched by monitors. Laser altimeter registers 50ft. Begins polling leg monitors to determine touch down. Latched noise spike read as touchdown. Engine shutdown at ~50ft. Mars 98: Climate Orbiter Mars Polar Lander

10 Model-based Programs Interact Directly with State Embedded programs interact with plant sensors/actuators: Read sensors Set actuators Model-based programs interact with plant state: Read state Write state Embedded Program S Plant Obs Cntrl Model-based Embedded Program S Plant Programmer must map between state and sensors/actuators. Model-based executive maps between sensors, actuators to states.

11 Reactive Model-based Programs Interact Directly with State S Plant Obs Cntrl Model-based Embedded Programs Model-based Executive S Plant Model State estimates Reactive Planning Mode Estimation State goals Reactive Model-based Programming Language: State assertion State query Conditional Execution Preemption Iteration Concurrent execution Closed Valve Open Stuckopen Stuckclosed OpenClose 0. 01 0.01 0.01 inflow = outflow = 0 Valve fails stuck closed Fire backup engine Thrust = On

12 Cooperative Model-based Programming How do we specify the allowed behaviors of cooperative robotic networks? (RMPL) How do we command cooperative networks? (this talk) How do we monitor cooperative networks? (next talk)

13 Outline Cooperative Vehicle Missions Model-based Programming Reactive Model-based Programming Language (RMPL) Temporal Plan Networks (TPN) Activity Planning (Kirk) Optional: Hybrid Activity/Path Planning

14 Properties: Teams are focused to a hierarchy complex strategies. Maneuvers are temporally coordinated. Novel events occur during critical phases. Quick response draws upon a library of contingencies. Example Scenario HOME TWO Enroute COLLECTION POINT RENDEZVOUS Diverge SCIENCE AREA 1’ SCIENCE AREA 3 Landing Site: ABC Landing Site: XYZ ONE SCIENCE AREA 1 Create Language with planner-like capabilities

15 Reactive Model-based Programming Idea: Describe team behaviors by starting with a rich concurrent, embedded programming language (RMPL,TCC, Esterel): c If c next A Unless c next A A, B Always A Sensing/actuation activities Conditional execution Preemption Full concurrency Iteration A [l,u] Timing Add temporal constraints: Choose {A, B} Contingency Add choice (non-deterministic or decision-theoretic):

16 Example Enroute Activity: Rendezvous Rescue Area Corridor 2 Corridor 1 Enroute

17 RMPL for Group-Enroute Group-Enroute()[l,u] = { choose { do { Group-Traverse- Path(PATH1_1,PATH1_2,PATH1_3,RE_POS)[l*90%,u*90%]; } maintaining PATH1_OK, do { Group-Traverse- Path(PATH2_1,PATH2_2,PATH2_3,RE_POS)[l*90%,u*90%]; } maintaining PATH2_OK }; { Group-Transmit(OPS,ARRIVED)[0,2], do { Group-Wait(HOLD1,HOLD2)[0,u*10%] } watching PROCEED }

18 RMPL for Group-Enroute Group-Enroute()[l,u] = { choose { do { Group-Traverse- Path(PATH1_1,PATH1_2,PATH1_3,RE_POS)[l*90%,u*90%]; } maintaining PATH1_OK, do { Group-Traverse- Path(PATH2_1,PATH2_2,PATH2_3,RE_POS)[l*90%,u*90%]; } maintaining PATH2_OK }; { Group-Transmit(OPS,ARRIVED)[0,2], do { Group-Wait(HOLD1,HOLD2)[0,u*10%] } watching PROCEED } Activities:

19 RMPL for Group-Enroute Group-Enroute()[l,u] = { choose { do { Group-Traverse- Path(PATH1_1,PATH1_2,PATH1_3,RE_POS)[l*90%,u*90%]; } maintaining PATH1_OK, do { Group-Traverse- Path(PATH2_1,PATH2_2,PATH2_3,RE_POS)[l*90%,u*90%]; } maintaining PATH2_OK }; { Group-Transmit(OPS,ARRIVED)[0,2], do { Group-Wait(HOLD1,HOLD2)[0,u*10%] } watching PROCEED } Conditionality and Preemption:

20 RMPL for Group-Enroute Group-Enroute()[l,u] = { choose { do { Group-Traverse- Path(PATH1_1,PATH1_2,PATH1_3,RE_POS)[l*90%,u*90%]; } maintaining PATH1_OK, do { Group-Traverse- Path(PATH2_1,PATH2_2,PATH2_3,RE_POS)[l*90%,u*90%]; } maintaining PATH2_OK } ; { Group-Transmit(OPS,ARRIVED)[0,2], do { Group-Wait(HOLD1,HOLD2)[0,u*10%] } watching PROCEED } Sequentiality: Concurrency:

21 RMPL for Group-Enroute Group-Enroute()[l,u] = { choose { do { Group-Fly- Path(PATH1_1,PATH1_2,PATH1_3,RE_POS)[l*90%,u*90%]; } maintaining PATH1_OK, do { Group-Fly- Path(PATH2_1,PATH2_2,PATH2_3,RE_POS)[l*90%,u*90%]; } maintaining PATH2_OK }; { Group-Transmit(OPS,ARRIVED)[0,2], do { Group-Wait(HOLD1,HOLD2)[0,u*10%] } watching PROCEED } Temporal Constraints:

22 RMPL for Group-Enroute Group-Enroute()[l,u] = { choose { do { Group-Traverse- Path(PATH1_1,PATH1_2,PATH1_3,RE_POS)[l*90%,u*90%]; } maintaining PATH1_OK, do { Group-Traverse- Path(PATH2_1,PATH2_2,PATH2_3,RE_POS)[l*90%,u*90%]; } maintaining PATH2_OK }; { Group-Transmit(OPS,ARRIVED)[0,2], do { Group-Wait(HOLD1,HOLD2)[0,u*10%] } watching PROCEED } Non-deterministic choice:

23 RMPL Interpreter KirkKirk IdeaIdea TitanTitan Dynamically selects among alternate executions, satisfies open conditions and checks schedulability, Selects execution times, monitors outcomes and plans contingencies. Reactive Temporal Planner Plan Runner (Hidden) States RMPL Program Commands Observables Mode EstimationReactive Planning Model of Networked Embedded Vehicles monitor activitiesmonitor activities diagnose plan failuresdiagnose plan failures

24 How do we provide fast, temporally flexible planning? Graph-based planners support fast planning. … but plans are totally order. Desire flexible plans based on simple temporal networks (e.g., HSTS, Muscetola et al.). How do we create temporally flexible plan graphs? Generalize simple temporal networks (temporal plan network TPN).

25 Kirk: Reactive Temporal Planner RMPL Compiler Temporal Plan Network (TPN) with STN Reactive Temporal Planner Selects schedulable execution threads of TPN Reactive Model-based Programming Language Concurrent Plan Plan = Execution threads related by Simple Temporal Net Represents all RMPL executions

26 Outline Cooperative Vehicle Missions Model-based Programming Reactive Model-based Programming Language (RMPL) Temporal Plan Networks (TPN) Activity Planning (Kirk) Optional: Hybrid Activity/Path Planning

27 Enroute Activity: 1 45 8 910 13 2 1112 Enroute Group Fly TraverseGroup Wait Group Transmit Activity (or sub-activity) Science Target Start with flexible plan representation

28 Enroute Activity: 1 45 8 2 Enroute [450,540] [405, 486] Group TraverseGroup Wait Group Transmit [0, 54] [0, 2] Activity (or sub-activity) Duration (temporal constraint) [0, ] [0, 0] Science Target Start with flexible plan representation

29 Enroute Activity: 3 1 45 8 910 13 2 671112 Enroute Group Fly Path Group Wait Group Transmit Activity (or sub-activity) Science Target TPN representation of Enroute activity and sub-activities

30 Enroute Activity: 3 1 45 8 2 Enroute [450,540] Group Traverse [405, 486] Group TraverseGroup Wait Group Transmit [0, 54] [0, 2] Activity (or sub-activity) Duration (temporal constraint) [0, ] [0, 0] Science Target Add conditional nodes Conditional node

31 Enroute Activity: 3 1 45 8 910 13 2 671112 Enroute [450,540] Group Traverse [405, 486] Group TraverseGroup Wait Group Transmit [0, 54] [0, 2] Activity (or sub-activity) Duration (temporal constraint) [0, ] [0, 0] Ask( PATH1 = OK) Ask( PATH2 = OK) Ask( EXPLORE = OK) Science Target Add temporally extended, symbolic constraints Symbolic constraint (Ask,Tell) Conditional node

32 Outline Cooperative Vehicle Missions Model-based Programming Reactive Model-based Programming Language (RMPL) Temporal Plan Networks (TPN) Activity Planning (Kirk) Optional: Hybrid Activity/Path Planning

33 Planning Group-Enroute 3 6 4 5 [405,486] Ask(PATH1=OK) 12 7 Ask(PATH2=OK) 8 [405,486] [450,540] Ask(PROCEED) 1 9 1010 [0,54] 1212 1313 [0,2] [0,  ] To Plan: Instantiate Group-Enroute Group-Enroute Group Traverse Group Wait Group Transmit Science Target

34 Planning Group-Enroute 3 6 4 5 [405,486] Ask(PATH1=OK) 12 7 Ask(PATH2=OK) 8 [405,486] [450,540] Ask(PROCEED) 1 9 1010 [0,54] 1212 1313 [0,2] [0,  ] 1415 Tell(PATH1=OK) [450,450] 1617 Tell(PROCEED) [200,200] s e [500,800] [10,10] [0,  ] To Plan: Instantiate Group-Enroute Add External Constraints (Tells) Group-Enroute Group Traverse Group Wait Group Transmit Science Target

35 Generates Schedulable Plan 3 6 4 5 [405,486] Ask(PATH1=OK) 12 7 Ask(PATH2=OK) 8 [405,486] [450,540] Ask(PROCEED) 11 9 10 [0,54] 12 13 [0,2] [0,  ] 1415 Tell(PATH1=OK) [450,450] 1617 Tell(PROCEED) [200,200] s e [500,800] [10,10] [0,  ] Group-Enroute Group Traverse Group Wait Group Transmit Science Target Trace consistent trajectories Check Schedulability Satisfy and Protect Asks To Plan: Instantiate Group-Enroute Add External Constraints

36 Planning Example 12 3456 789 101112 1314 Find paths from start-node to end-node StartEnd 15161718

37 Planning Example 12 3456 789 101112 1314 Not a decision-node: Follow all outarcs StartEnd 15161718

38 Planning Example 12 3456 789 101112 1314 Not a decision-node: Follow all outarcs StartEnd 15161718

39 Planning Example 12 3456 789 101112 1314 Not a decision-node: Follow all outarcs StartEnd 15161718

40 Planning Example 12 3456 789 101112 1314 Decision-node: Select a single outarc StartEnd 15161718

41 Planning Example 12 3456 789 101112 1314 Not a decision-node: Follow all outarcs StartEnd 15161718

42 Planning Example 12 3456 789 101112 1314 Continue StartEnd 15161718

43 Planning Example 12 3456 789 101112 1314 Not a decision-node: Follow all outarcs StartEnd 15161718

44 Planning Example 12 3456 789 101112 1314 Continue StartEnd 15161718

45 Planning Example 12 3456 789 101112 1314 StartEnd 15161718

46 Temporal Constraint Consistency 12 3456 789 101112 1314 Don’t test consistency at each step. Only when a path induces a cycle, check for negative cycle in the STN distance graph 15161718 [18,20] [0,0] [0,  ] [2,3] [15,16] [4,6] [5,5][3,8]

47 Temporal Constraint Consistency 12 3456 789 101112 1314 Example: Inconsistent 15161718 [18,20] [0,0] [0,  ] [2,3] [15,16] [4,6] [5,5][3,8]

48 Temporal Constraint Consistency 12 3456 789 101112 1314 Backtrack to choice 15161718 [18,20] [0,0] [0,  ] [2,3] [15,16] [4,6] [5,5][3,8] [0,0] [12,13] [0,0]

49 Temporal Constraint Consistency 12 3456 789 101112 1314 Complete paths 15161718 [18,20] [0,0] [0,  ] [2,3] [15,16] [4,6] [5,5][3,8] [0,0] [12,13] [0,0]

50 How Do Handle Asks? 3 6 4 5 [405,486] Ask(PATH1=OK) 12 7 Ask(PATH2=OK) 8 [405,486] [450,540] Ask(PROCEED) 1 9 1010 [0,54] 1212 1313 [0,2] [0,  ] 1415 Tell(PATH1=OK) [450,450] 1617 Tell(PROCEED) [200,200] s e [500,800] [10,10] [0,  ] Unconditional Planning: Guarantee satisfaction at compile time. Treatment similar to causal-link planning Group-Enroute Group Traverse Group Wait Group Transmit Science Target

51 Satisfying Asks Compute bounds on activities. Link ask to equivalent, overlapping tell. Constrain tell to contain ask. 5 789 101112 6 {4,6} {6,9} {5,8}{7,11} {7,10} {8,11} ask(c) tell(c)

52 Avoiding Threats Identify overlapping Inconsistent activities. 5 789 101112 6 {4,6} {6,9} {5,8}{7,11} {7,10} {8,11} tell(c) tell(  c)

53 Symbolic Constraint Consistency Promote or demote 5 789 101112 6 {4,6} {7,9} {5,8}{7,9} {7,10} {8,11} tell(c) tell(  c) [0,infb]

54 Outline Cooperative Vehicle Missions Model-based Programming Reactive Model-based Programming Language (RMPL) Temporal Plan Networks (TPN) Activity Planning (Kirk) Optional: Hybrid Activity/Path Planning

55 Enroute Activity: 3 1 45 8 910 13 2 67 1112 Enroute [450,540] Group Traverse [405, 486] Group TraverseGroup Wait Group Transmit [0, 54] [0, 2] [0, ] [0, 0] Ask( PATH1 = OK) Ask( PATH2 = OK) Ask( PROCEED) Traverse to Science Target Science Target Closer look at Group Traverse sub-activity

56 Group Traverse sub-activity: 3 4 8 67 [0, 0] Ask( PATH2 = OK) 5 Traverse through way points to science target Group Traverse [405, 486] [0, 0]

57 3 4 8 67 5 One obstacle between nodes 4 and 5 Two Obstacles between nodes 6 and 7 [0, 0] Group Traverse sub-activity: ObstacleObstacle ObstacleObstacle ObstacleObstacle

58 How do we optimally select activities and paths? Current Research: Perform global path planning using Rapidly-exploring Random Trees (RRTs) (la Valle). Search for globally optimal plan by unifying TPN & RRT graphs, and by searching hybrid graph best first. Perform local kino-dynamic path planning along path segments using hybrid maneuver automata (Frazzoli, Dahleh, Feron).

59 3 4 8 67 [0, 0] 5 Non-explicit representations of obstacles obtained from an incremental collision detection algorithm [0, 0] Group Traverse sub-activity:

60 RRT: Example 3 4 8 67 [0, 0] 5 Path 1 Path 2

61 3 4 8 67 [0, 0] 5 x init x goal Assume rovers take Path 1: Path 1 Path 2 RRT: Example

62 45 x init Path 1 x goal X obs RRT: Example

63 45 x init Path 1 x goal X obs RRT: Example

64 45 x init Path 1 x goal X obs RRT: Example

65 45 x init Path 1 x goal X obs RRT: Example

66 45 x init Path 1 x goal X obs RRT: Example

67 45 x init Path 1 x goal X obs RRT: Example

68 45 x init Path 1 x goal X obs Common Node RRT: Example

69 45 x init Path 1 x goal X obs RRT: Example

70 45 x init Path 1 x goal X obs RRT: Example

71 45 x init Path 1 x goal X obs RRT: Example

72 Current Status Kirk demonstrated on cooperative scenario using UAV simulation. Development on Multi-Rover testbed currently in progress. Distributed hybrid activity/path planning in progress. Selected for NASA Space Technology 7, Phase A together with IDEA (Muscettola)

73 Model-based Cooperative Programming Goal: Fast, mission-directed coordination of teams of vehicles acting in an uncertain environments. Solution: New middle ground between embedded programming, task decomposition execution, and temporal planning. Rich embedded language,RMPL, for describing complex concurrent team strategies extended to time and contingency. Kirk Interpreter “looks” for schedulable threads of execution before “leaping” to execution. Temporal Plan Network provides a flexible, temporal, graph- based planning paradigm built upon Simple Temporal Nets. Interpreter “leaps” through flexible execution (Nicola talk). Current work towards unifying activity planning, global path planning and kino-dynamics.

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