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MIT 16 412J - Lecture Notes

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Temporal Planning in Space1Brian C. Williams andRobert Morris (guest lect.)16.412J/6.834J March 2nd, 2005based on: “Application of Mapgen to MER,” by Kanna Rajan“Handling Time:Constraint-based Interval Planning,” by David E. SmithOutlineBased on slides by Dave Smith, NASA Ames• Operational Planning for the Mars Exploration Rovers• Review of Least Commitment Planning• Constraint-based Interval Planning• Temporal Constraint Networks• Temporal Constraints with PreferenceMars Exploration Rovers – Jan. 2004 - ?Mars Exploration Rovers – Jan. 2004 - ?Mission Objectives:• Learn about ancient water and climate on Mars.• For each rover, analyze a total of 6-12 targets– Targets = natural rocks, abraded rocks, and soil•Drive 200-1000 meters per roverMission Objectives:• Learn about ancient water and climate on Mars.• For each rover, analyze a total of 6-12 targets– Targets = natural rocks, abraded rocks, and soil•Drive 200-1000 meters per roverMini-TESPancamNavcamRock Abrasion ToolMicroscopic ImagerMossbauer spectrometerAPXSMars Exploration RoverSurface Operations ScenarioTargetDay 4During the DayScience ActivitiesDay 1Long-Distance Traverse (<20-50 meters)Day 2Initial Position; Followed by “Close Approach”During the DayAutonomous On-Board Navigation Changes, as neededDay 2 Traverse Estimated Error CircleDay 3Science Prep(if Required)Day 2 Traverse Estimated Error CircleActivity NameDuration1011121314151617181920212223012345678910111213141516171819202122230123456789DTE4.500.75DTE periodDFENight Time Rover Operations16.97Night Time Rover OperationsSleepWakeupPre-Comm Session Sequence Plan ReviewCurrent Sol Sequence Plan Review1.501.50Current Sol Sequence Plan ReviewPrior Sol Sequence Plan Review2.00Prior Sol Sequence Plan ReviewReal-TIme Monitoring4.500.75Real-TIme MonitoringReal-TIme MonitoringDownlink Product Generation...2.75Downlink Product GenerationTactical Science Assessment/Observation Planning5.00Tactical Science Assessment/Observation PlanningScience DL Assessment Meeting1.00Science DL Assessment MeetingPayload DL/UL Handoffs0.50Payload DL/UL HandoffsTactical End-of-Sol Engr. Assessment & Planning5.50Tactical End-of-Sol Engr. Assessment & PlanningDL/UL Handover Meeting0.50DL/UL Handover MeetingSkeleton Activity Plan Update2.50Skeleton Activity Plan UpdateSOWG Meeting2.00SOWG MeetingUplink Kickoff Meeting0.25Uplink Kickoff MeetingActivity Plan Integration & Validation1.75Activity Plan Integration & ValidationActivity Plan Approval Meeting0.50Activity Plan Approval MeetingBuild & Validate Sequences2.25Build & Validate SequencesUL1/UL2 Handover1.00UL1/UL2 HandoverComplete/Rework Sequences2.50Complete/Rework SequencesMargin 10.75Margin 1Command & Radiation Approval0.50Command & Radiation ApMargin 21.25Margin 2Radiation0.50RadiationMCT Team7.004.00One day in the life of a Mars roverDownlink Assessment Science Planning Sequence Build/Validation UplinkCourtesy: Jim EricksonMAPGEN: AutomatedScience Planning for MEREUROPAAutomated Planning SystemEUROPAAutomated Planning SystemScienceNavigationEngineeringResourceConstraintsDSN/TelcomFlight RulesSequence BuildPlanning Lead: Kanna Rajan (ARC)Science TeamNext Challenge: Mars Smart Lander (2009)Next Challenge: Mars Smart Lander (2009)Mission Duration: 1000 daysTotal Traverse: 3000-69000 metersMeters/Day: 230-450Science Mission: 7 instruments, sub-surface science package (drill, radar), in-situ sample “lab”Technology Demonstration:(2005).Course Challenge: 16.413 Fall 03Course Challenge: 16.413 Fall 03• What would it be like to operate MER if it was fully autonomous?Potential inspiration for course projects: • Demonstrate an autonomous MER mission in simulation, and in the MIT rover testbed.• What would it be like to operate MER if it was fully autonomous?Potential inspiration for course projects: • Demonstrate an autonomous MER mission in simulation, and in the MIT rover testbed.OutlineBased on slides by Dave Smith, NASA Ames• Operational Planning for the Mars Exploration Rovers• Review of Least Commitment Planning• Constraint-based Interval Planning• Temporal Constraint Networks• Temporal Constraints with PreferencePlanningFind:program of actions that achieves the objectivePlanningFind:program of actions that achieves the objectivegoalspartially-ordered settypically unconditionalParadigmsClassical planning(STRIPS, operator-based, first-principles)“generative”Hierarchical Task Network planning“practical” planningMDP & POMDP planningplanning under uncertaintyThe Classical RepresentationP1P2P3P4Initial Conditions:Operators:Oppre1pre2pre3eff1eff2Goal1Goal2Goal3Goals:Simple Spacecraft ProblemObservation-1targetinstrumentsObservation-2Observation-3Observation-4…calibratedpointingExampleInit Actions GoalpCcpCIACTy¬pxpypxImcpxIx16.410/13: Solved using Graph-based Planners (Blum & Furst)Some STRIPS OperatorsBased on slides by Dave Smith, NASA AmesTakeImage (?target, ?instr):Pre: Status(?instr, Calibrated), Pointing(?target)Eff: Image(?target)Calibrate (?instrument):Pre: Status(?instr, On), Calibration-Target(?target), Pointing(?target)Eff: ¬Status(?inst, On), Status(?instr, Calibrated)Turn (?target):Pre: Pointing(?direction), ?direction ≠ ?targetEff: ¬Pointing(?direction), Pointing(?target)Partial Order Causal Link Planning(SNLP, UCPOP)FIA1. Select an open condition 2. Choose an op that can achieve itLink to an existing instanceAdd a new instance 3. Resolve threatsImcpAIAFpCCImIAFcpACpCImIAFcpASTA¬pCCpCImIAFcpASpCTA¬pCCpCImIAFcpASpCOutlineBased on slides by Dave Smith, NASA Ames• Operational Planning for the Mars Exploration Rovers• Review of Least Commitment Planning• Constraint-based Interval Planning• Temporal Constraint Networks• Temporal Constraints with PreferenceAn Autonomous Science ExplorerBased on slides by Dave Smith, NASA AmesObservation-1prioritytime windowtargetinstrumentsdurationObservation-2Observation-3Observation-4…Objective:maximize science returnBased on slides by Dave Smith, NASA AmesComplicationsObservation-1prioritytime windowtargetinstrumentsdurationObservation-2Observation-3Observation-4…calibrationtarget1target2…consumables:fuelpowerdata storagecryogenangle between targets⇒ turn durationObjective:maximize science returnlinkedBased on slides by Dave Smith, NASA AmesLimitations of Classical Planningwith Atomic Actions (aka STRIPS)Instantaneous actionsNo temporal constraintsNo concurrent actionsNo continuous quantitiesNeeded


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