会 MERS Executing Model-based Programs sing Graph-based Temporal Planning Prof Brian C. williams February 23rd, 2004 16.412/6.834 Cognitive Robotics based on [Kim, williams Abramson, IJCAIO1I Outline MERS Model-based Programming Cooperative Vehicle missions The Reactive Model-based Programming Language (repl Temporal Plan Networks(TPN) Activity Planning(Kirk Unifying Activity and Path Planning
Executing Model-based Programs Using Graph-based Temporal Planning Prof. Brian C. Williams February 23rd, 2004 16.412/6.834 Cognitive Robotics based on [Kim,Williams & Abramson, IJCAI01] Outline • Model-based Programming • Cooperative Vehicle Missions • The Reactive Model-based Programming Language (RMPL) • Temporal Plan Networks (TPN) • Activity Planning (Kirk) • Unifying Activity and Path Planning
会 Why model-based programming Q MERS 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 mage courtesy of JPL touchdown Engine shutdown at -50ft Mars Climate Orbiter · Mars polar lander Create Embedded Languages That Reason on the Fly from Commonsense models Model-based Programs MERS lai Interact Directly with State Embedded programs interact with Model-based programs plant sensors/actuators interact with plant state · Read sensors Read state Set actuators Write state ,,, Model-based Embedded Program Embedded Program obs Cntrl Plant Plant Programmer must map between Model-based executive maps state and sensors/actuators between sensors actuators to states
Why Model-based Programming? Create Embedded Languages That Reason on the Fly 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 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. Image courtesy of JPL
Example: The model-based program sets the state to thrusting, and the deductive controller Oxidizer tank Fuel tank →轻柱 Deduces that Plans actions thrust is off. and the engine is healthy six valves Deduces that a valve failed -stuck closed Determines that valves on the backup engine will achieve thrust. and plans needed actions RMPL Model-based Program Titan Model-based Executive Control Program Executes concurrent Pr Generates target goal states Queries(hidden) states conditioned on state estimates Asserts(hidden) state System Model State estimates State goals Tracks Tracks least e Valve plant states cost goal states Stuck Closed t。+4 closed/OE Stuck bservations Commands inflow outflow= Plant
Example: The model-based program sets the state to thrusting, and the deductive controller . . . . Determines that valves on the backup engine will achieve thrust, and plans needed actions. Deduces that a valve failed - stuck closed Plans actions to open six valves Oxidizer tank Oxidizer tank Fuel tank Fuel tank Deduces that thrust is off, and the engine is healthy Control Sequencer Deductive Controller System Model Observations Commands Control Program Plant RMPL Model-based Program Titan Model-based Executive State estimates State goals Generates target goal states conditioned on state estimates Mode Estimation Mode Reconfiguration Tracks likely plant states Tracks least cost goal states z Executes concurrently z Preempts z Queries (hidden) states z Asserts (hidden) state Closed Valve Open Stuck open Stuck closed Open Close 0. 01 0. 01 0.01 0.01 inflow = outflow = 0
Modeling complex behaviors through Probabilistic concurrent constraint automata Valve Stuck Stuck Closed closed inflow outflow=0 Complex, discrete behaviors modeled through concurrency, hierarchy and non-determinism Anomalies and uncertainty modeled by probabilistic transitions Physical interactions modeled by discrete and continuous constraints Outline MERS Model-based Programming Cooperative Vehicle Missions The Reactive Model-based Programming Language (repl Temporal Plan Networks(TPN) Activity Planning(Kirk Unifying Activity and Path Planning
Closed Valve Open Stuck open Stuck closed Open Close 0. 01 0. 01 0.01 0.01 inflow = outflow = 0 Modeling Complex Behaviors through Probabilistic Concurrent Constraint Automata • Complex, discrete behaviors • modeled through concurrency, hierarchy and non-determinism. • Anomalies and uncertainty • modeled by probabilistic transitions • Physical interactions • modeled by discrete and continuous constraints Outline • Model-based Programming • Cooperative Vehicle Missions • The Reactive Model-based Programming Language (RMPL) • Temporal Plan Networks (TPN) • Activity Planning (Kirk) • Unifying Activity and Path Planning
Cooperative Search and Rescue MERS High-level vehicle coordination Fast Agile Maneuvering Cooperative Mars Exploration MERS How do we coordinate heterogeneous teams of orbiters rovers and air vehicles to perform globally optimal science exploration?
Cooperative Search and Rescue • High-level vehicle coordination • Fast Agile Maneuvering Cooperative Mars Exploration How do we coordinate heterogeneous teams of orbiters, rovers and air vehicles to perform globally optimal science exploration?