Space Systems & Artificial Intelligence Laboratories Massachusetts Institute of Technology

Computational Challenges for Model-based Autonomous Systems

Artificial Intelligence & Space Systems LaboratoriesMassachusetts Institute of Technology

Prof. Brian C. Williams

Space Systems & Artificial Intelligence Laboratories Massachusetts Institute of Technology

Idea: Support programmers with embedded languages that avoid commonsense mistakes, by reasoning from hardware models.

Polar Lander Leading Diagnosis:

• Legs deployed during descent.

• Noise spike on leg sensors latched by software monitors.

• Laser altimeter registers 50ft.

• Begins polling leg monitors to determine touch down.

• Latched noise spike read as touchdown.

• Engine shutdown at ~50ft.

Reactive Model-based Programming

Model-based Executives automate all reasoning about system interactions.

•Scheduling

•Command confirmation

•Diagnosis

•Commanding

•Configuration

•Repair . . .

Space Systems & Artificial Intelligence Laboratories Massachusetts Institute of Technology

Model-based Programs Interact Directly with State

Embedded programs interact withplant sensors and actuators:

• Read sensors

• Set actuators

Embedded Program

SPlant

Obs Cntrl

Programmers must map between states and sensors/actuators.

Model-based programs interact with plant state:

• Read state

• Write state

Model-basedEmbedded Program

SPlant

Model-based executives map automatically between states and sensors/actuators.

S’Model-based Executive

Obs Cntrl

Space Systems & Artificial Intelligence Laboratories Massachusetts Institute of Technology

Example: The model-based program sets the state to thrusting, and the model-based executive . . . .

Determines that valveson the backup engine

will achieve thrust, andplans needed actions.

Deduces that a valve failed - stuck closed

Plans actionsto open

six valves

Fuel tankFuel tankOxidizer tankOxidizer tank

Deduces thatthrust is off, and

the engine is healthy

ClosedClosed

ValveValveOpenOpen StuckStuck

openopen

StuckStuckclosedclosed

OpenOpen CloseClose

0. 010. 01

0. 010. 01

0.010.01

0.010.01

inflow = outflow = 0

Modeling Complex Behaviors through Probabilistic Concurrent Constraint Automata

• Complex, discrete behaviors

• Anomalies and uncertainty

• Physical interactions

• Timing

• modeled through concurrency, hierarchy and non-determinism.

• modeled by probabilistic transitions

• modeled by discrete and continuous constraints

• modeled by simple temporal networks

System Model

CommandsObservations

Control Program

PlantDeductive Controller

Model-based Executive(Livingstone, Titan, Kirk..)

Model-based Program

Model-based Autonomy Architecture

State goalsState estimates

Tracks likely state

trajectories

Mode Estimation Mode Reconfiguration

Findsbest

target

Plansreactively

Control Program Sequencer

Performs lazy schedulingPerforms lazy scheduling

Searches for optimal feasible threads of execution

Searches for optimal feasible threads of execution

PlansPlanFailures

Computational Challenges:• Propositional Satisfiability• Optimal CSPs• Graph-based Planning• Scheduling

Executes concurrentlyPreemptsAsserts and queries statesChooses based on rewardExpresses temporal andresource constraints

ClosedClosed

ValveValveOpenOpen StuckStuck

openopen

StuckStuckclosedclosed

OpenOpen CloseClose

0. 010. 01

0. 010. 01

0.010.01

0.010.01

inflow = outflow = 0

Space Systems & Artificial Intelligence Laboratories Massachusetts Institute of Technology

Future autonomy requires reasoning about hybrid discrete/continuous systems

Detecting subtle failures

Compute hybrid of:• temporal constraint problem • mixed integer linear program

Compute hybrid of:• HMM belief update• Kalman filtering

Coordinating fleets of agile vehicles

XY

d

x x d

x x d

y y d

y y d

p q

q p

p q

q p

− ≥

− ≥

− ≥

− ≥

or

or

or

x x d Mb

x x d Mb

y y d Mb

y y d Mb

b

p q pq

q p pq

p q pq

q p pq

pqkk

− ≥ −

− ≥ −

− ≥ −

− ≥ −

≤=∑

1

2

3

4

1

43

and

and

and

and Probabilistic mode transition

old estimate: newestimate:

continuous state evolution within mode