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System RMPL Activity plan model Kirk Planner / Scheduler Plan Runner Schedulable / consistent plan re-plan request commands status update Compiler Converter RMPL program HCA Model RRTPN graph Robot Interface Autonomous Vehicles Optimal RRTPN Search Fast planning Scheduling

System RMPL Activity plan model Kirk Planner / Scheduler Plan Runner Schedulable / consistent planre-plan request commandsstatus update Compiler Converter

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Page 1: System RMPL Activity plan model Kirk Planner / Scheduler Plan Runner Schedulable / consistent planre-plan request commandsstatus update Compiler Converter

System

RMPL

Activity planmodel

Kirk

Planner / Scheduler

Plan Runner

Schedulable / consistent plan re-plan request

commands status update

Compiler

Converter

RMPL program

HCA Model

RRTPN graph

Robot Interface

Autonomous Vehicles

Optimal RRTPN Search

Fast planningScheduling

Page 2: System RMPL Activity plan model Kirk Planner / Scheduler Plan Runner Schedulable / consistent planre-plan request commandsstatus update Compiler Converter

• Locations in RMPL/TPN can grow RRTs in effort to satisfy the location constraints

Tell(Robot.location = C)

(start)

(goal)

RRT

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

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

total time = 100

[95,105]

Tell(Not(Robot.location = C)) [95,105]

Signal that Robot cannot be going to any other location during this time interval

RRTPN

RRTTPN planning model Location constraints Memory-bounded optimal plan search

Page 3: System RMPL Activity plan model Kirk Planner / Scheduler Plan Runner Schedulable / consistent planre-plan request commandsstatus update Compiler Converter

Activity: ANW1.Apply_Controls( control inputs ) [l,u]

Tell(ANW1.location = daycare) [L,U]

RRTPN

RRTTPN planning model Location constraints Memory-bounded optimal plan search

Page 4: System RMPL Activity plan model Kirk Planner / Scheduler Plan Runner Schedulable / consistent planre-plan request commandsstatus update Compiler Converter

Location Constraints

2. Simultaneously execute on-board activities and navigate

1. Make a robot go to a locationRMPL (sequence ((ANW1.location = daycare) [10,20]) )

[10,20]

Tell(ANW1.location = corridor)

RMPL (parallel ((ANW1.location = corridor) [10,20]) (( ANW1.Take_Pictures()) )

RRTPN

Take_Pictures( )[5,15]

[10,20]

Tell(ANW1.location = daycare)

RRTPN

[0,0]

[0,0]

[0,0]

[0,0]AND-start

RRTTPN planning model Location constraints Memory-bounded optimal plan search

AND-end

Page 5: System RMPL Activity plan model Kirk Planner / Scheduler Plan Runner Schedulable / consistent planre-plan request commandsstatus update Compiler Converter

Location Constraints

4. Choose which location the robot should visit

3. To perform an activitiy require a robot to be at a locationRMPL (if -thennext(ANW-1.location = lab)

((Lower_Chembots()) [15,30]) )

RRTPN

RMPL (choose ((ANW-1.location = daycare) [10,20]) ((ANW-1.location = cafeteria) [15,28]) ((ANW-1.location = lab) [5,20]) )

[0,0]

Ask(ANW1.location = lab)

Lower_Chembots( ) [15,30]

[10,20]

Tell(ANW1.location = daycare)

[5,20]

Tell(ANW1.location = lab)

[15,28]

Tell(ANW1.location = cafeteria)

RRTPN

Decision (OR) node

RRTTPN planning model Location constraints Memory-bounded optimal plan search

Page 6: System RMPL Activity plan model Kirk Planner / Scheduler Plan Runner Schedulable / consistent planre-plan request commandsstatus update Compiler Converter

RMPLMission Strategies

RMPLMission Strategies

RobotModelsRobot

Models

StrategySelectionStrategySelection

Global PathPlanning

Global PathPlanning

KinodynamicManeuverPlanning

KinodynamicManeuverPlanning

Page 7: System RMPL Activity plan model Kirk Planner / Scheduler Plan Runner Schedulable / consistent planre-plan request commandsstatus update Compiler Converter

Search Building steps

• Enter building through windowI or windowII

• Fly to labA

• Deploy chem bots