Outline

Outline

• Deep Space One and Remote Agent• Model-based Execution• OpSat and the ITMS• Model-based Reactive Planning• Space Robotics

Modes Controlled Through Interactions

drivercomputer buscontrol

remoteterminal

driverremote

terminal

How do we open a valve?


drivercomputer buscontrol

remoteterminal

driverremote

terminal

MRMI

Command

Configuration goals

Model

goal statecurrent state

ReactivePlanner

computer buscontrol

remoteterminal

remoteterminal

driver

driver


• Irreversible actions should be avoided by reactive planners.

• Component schematics tend not to have loops goals are serializable

Solution:• Work goals from outputs to inputs.

• Achieve goals serially.

Indirect controllability

inflow = outflow

Closed

Open

cmd = open cmd = closecmd =off

On

Off

cmd= on cmd = off

cmd = reset

vcmdin = vcmdout

vcmdout(Driver) = cmd(Valve)

• Device states are not directly controllable

Compilation

driver = oncmd= open

driver = oncmd = close

inflow = outflow

Closed

Open

cmd =off

On

Off

cmd= on cmd = off

cmd= reset

vcmdin = vcmdout

vcmdout(Driver) = cmd(Valve)

• Remove intermediate variables using implicate generation

– becomes a STRIPS-like problem

– implicate generation done using conflict directed best first search

Concurrent policies

driver = on

cmd = opendriver = oncmd = close

Closed

Open

fail

Goal

fail

driver = oncmd = open

idle

idledriver = oncmd = close

Current

Open

Closed

Stuck

Open Closed

• Convert resulting transition system into a local policyEncoding is extremely compact

• Ensure only reversible transitions are taken

Goal

Currentopen closed

open

closed

stuck fail fail

idle

idledr = oncmd=open

dr = oncmd=close

GoalCurrent on off

on

off

resetfailure

cmd=reset

idle

idlecmd = on

cmd = off

cmd=reset

off

Cmd

closedGoal:

Goal

Currentopen closed

open

closed

stuck fail fail

idle

idledr = oncmd=open

dr = oncmd=close

GoalCurrent on off

on

off

resetfailure

cmd=reset

idle

idlecmd = on

cmd = off

cmd=reset

off

Cmd

closedGoal:

cmd = on

Goal

Currentopen closed

open

closed

stuck fail fail

idle

idledr = oncmd=open

dr = oncmd=close

GoalCurrent on off

on

off

resetfailure

cmd=reset

idle

idlecmd = on

cmd = off

cmd=reset

off

Cmd

closedGoal:

cmd = close

Goal

Currentopen closed

open

closed

stuck fail fail

idle

idledr = oncmd=open

dr = oncmd=close

GoalCurrent on off

on

off

resetfailure

cmd=reset

idle

idlecmd = on

cmd = off

cmd=reset

off

Cmd

closedGoal:

Driver Fails!cmd = close

Goal

Currentopen closed

open

closed

stuck fail fail

idle

idledr = oncmd=open

dr = oncmd=close

GoalCurrent on off

on

off

resetfailure

cmd=reset

idle

idlecmd = on

cmd = off

cmd=reset

off

Cmd

closedGoal:

cmd = reset

Goal

Currentopen closed

open

closed

stuck fail fail

idle

idledr = oncmd=open

dr = oncmd=close

GoalCurrent on off

on

off

resetfailure

cmd=reset

idle

idlecmd = on

cmd = off

cmd=reset

off

Cmd

closedGoal:

cmd = close

Goal

Currentopen closed

open

closed

stuck fail fail

idle

idledr = oncmd=open

dr = oncmd=close

GoalCurrent on off

on

off

resetfailure

cmd=reset

idle

idlecmd = on

cmd = off

cmd=reset

off

Cmd

closedGoal:

cmd = off

Goal

Currentopen closed

open

closed

stuck fail fail

idle

idledr = oncmd=open

dr = oncmd=close

GoalCurrent on off

on

off

resetfailure

cmd=reset

idle

idlecmd = on

cmd = off

cmd=reset

off

Cmd

closedGoal:

Success!cmd = off

Complexity of Reactive Planning

• Worst Case per action: Depth * Sub-goal branch factor

• Average Cost per action: Sub-goal branch factor

Valve1 = open Valve2 = open Drv1 = off Drv2 = off

Drv1 = on

CU = on

CU = on

Drv2 = on

CU = on

CU = on

CU = on CU = on

Complexity Analysis Details

• Average Cost per action: Subgoal branch factor– Avg cost = compute time / plan length

– Each edge of the goal/subgoal tree is traversed twice.Compute time = 2 * E * B.

– Each node of the goal / subgoal tree generates one action.plan length = N

– Avg cost = O(E/N)

– In the worst case E < = 2 * N.

– Average Cost per action = O(B* N/N) = O(B)

Subgoals

Outline

• Deep Space One and Remote Agent• Model-based Execution• OpSat and the ITMS• Model-based Reactive Planning• Space Robotics

Current Livingstone Testbeds

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are needed to see this picture.

TechSat21 &Gflops TestbedMIT SSL

Spheres MIT SSL

PortableSatellite Assistant Ames

Conclusions

• Space is opening its doorway to a new generation of agile, highly independent explorers.

• To survive decades of operation they must orchestrate complex regulatory and immune

systems.

• Model-based autonomy supports rapid prototyping through model-based programming

and executives.

• A model-based executive is a kind of stochastic optimal controller that makes extensive

use of deduction to observe and control on the fly.

• The core is OPSAT, a real-time, combinatorial optimization algorithm with logical

feasibility constraints.

• Fast, model-based reactive planning is achieved through knowledge compilation and by

exploiting structural and safety constraints.

Outline

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