Robotic Space Explorers

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Robotic Space Explorers

• Brian C. Williams
• Space Systems Lab
• Artificial Intelligence Lab, MIT

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Marskokhod at NASA Ames research center
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Smart Buildings at CMU Xerox PARC
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Ecological Life SupportFor Mars Exploration
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Portable Satellite Assistant
Courtesy of Yuri Gawdiak, NASA Ames
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MIT SSL
courtesy Dave Miller, SSL MIT
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Robotic Webs
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Unmanned Air Vehicles
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Intelligence Embedded at all Levels

• Behavior-based robotics Subsumption
• Reinforcement learning and MDPS
• Classical planning and execution
• Model-based diagnosis and execution
• Mission-level planning
• Robotic path planning
• Probabilistic monitoring and
• Decision-theoretic planning
• Multi-agent coordination

Increased Reasoning
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To Boldly Go Where No AI System Has Gone Before

• A Story of Survival

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Started January 1996 Launch Fall 1998
courtesy JPL
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Remote Agent Team Members

• Douglas Bernard JPL
• Steve Chien JPL
• Greg Dorais Ames
• Julia Dunphy JPL
• Dan Dvorak JPL
• Chuck Fry Ames
• Ed Gamble JPL
• Erann Gat JPL
• Othar Hansson Thinkbank
• Jordan Hayes Thinkbank
• Bob Kanefsky Ames
• Ron Keesing Ames
• James Kurien Ames
• Bill Millar Ames
• Sunil Mohan Formida
• Paul Morris Ames

• Nicola Muscettola Ames
• Pandurang Nayak Ames
• Barney Pell Ames
• Chris Plaunt Apple
• Gregg Rabideau JPL
• Kanna Rajan Ames
• Nicolas Rouquette JPL
• Scott Sawyer LMMS
• Rob Sherwood JPL
• Reid Simmons CMU
• Ben Smith JPL
• Will Taylor Ames
• Hans Thomas Ames
• Michael Wagner 4th Planet
• Greg Whelan CMU
• Brian C. Williams Ames
• David Yan Stanford

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I am a HAL 9000 computer production number three.
I became operational at the H.A.L. plant in
Urbana, Illinois on January 12, 1997.
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courtesy NASA
International Space Station 1998-2002
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Our vision in NASA is to open the Space
Frontier. When people think of space, they
think of rocket plumes and the space shuttle.
But the future of space is in information
technology. We must establish a virtual
presence, in space, on planets, in aircraft and
spacecraft. - Daniel S. Goldin, NASA
Administrator Sacramento, California, May 29, 1996

• Motive Astrobiology Origins Programs
• Means New Millennium Program
• Smarts Autonomous Reasoning

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Motive Primitive life on Early Mars?
1997 Mars Pathfinder and Sojourner
courtesy JPL
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New Means
Mars Pathfinder, 1997
courtesy JPL
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New Means Mars Airplane
courtesy NASA Ames
courtesy NASA Lewis
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Cryobot Hydrobot
Motive life under Europa?
courtesy JPL
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Formation Flying Optical Interferometer (ST3)
Motive Earth-like Planets Around Other Stars?
courtesy JPL
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Four launches in 7 months
Mars Climate Orbiter 12/11/98
Mars Polar Lander 1/3/99
QuickSCAT 6/19/98
Stardust 2/7/99
courtesy of JPL
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Traditional spacecraft commanding
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How Will They Survive?

• Vanished
• Mars Observer
• Mars Polar Lander
• Miscommanded
• Clementine
• Mars Climate Orbiter

courtesy of JPL
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STS-93 Hydrogen Leak

• Symptoms
• Engine temp sensor high
• LOX level low
• GNC detects low thrust
• H2 level low (???)
• Problem Liquid hydrogen leak
• Effect
• LH2 used to cool engine
• Engine runs hot
• Consumes more LOX

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Intelligent Embedded Systems Cassini

• 7 year cruise
• 150 - 300 ground operators
• 1 billion
• 7 years to build

Faster, Better, Cheaper

• 150 million
• 2 year build
• 0 ground ops

Cassini Maps Titan
courtesy JPL
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Ames-JPL NewMaap New Millennium Advanced
Autonomy Prototype

• no Earth Comm
• 1 hr insertion window
• engines idle for several years
• moves through ring plane

July - November, 1995
courtesy JPL
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(No Transcript)
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Reconfiguring for a Failed Engine
Fuel tank
Oxidizer tank
Open four valves
Valve fails stuck closed
Fire backup engine
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AI in the pre-90s Reservations about Embedded
Systems being Intelligent

• For reactive systems proving theorems is out
  of the question Agre Chapman 87
• Diagnostic reasoning from a tractable model is
  largely well understood. However we dont know
  how to model complex behavior... Davis
  Hamscher 88
• Commonsense equations are far too general for
  practical use. Sacks Doyle 91

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Houston, We have a problem ...

• Quintuple fault occurs (three shorts, tank-line
  and pressure jacket burst, panel flies off).
• Mattingly works in ground simulator to identify
  new sequence handling severe power limitations.
• Mattingly identifies novel reconfiguration,
  exploiting LEM batteries for power.
• Swaggert Lovell work on Apollo 13 emergency rig
  lithium hydroxide unit.

courtesy of NASA
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Challenge Thinking Through Interactions
Programmers must reason through system-wide
interactions to generate codes for

• command confirmation
• goal tracking
• detecting anomalies
• isolating faults
• diagnosing causes

• hardware reconfig
• fault recovery
• safing
• fault avoidance
• control coordination

Equally problematic at mission operations level
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Model-based Autonomy

• Programmers generate breadth of functions from
  commonsense models in light of mission goals.
• Model-based Programming
• Program by specifying commonsense, compositional
  declarative models.
• Model-directed Planning Execution
• Provide services that reason through each type of
  system interaction from models.
• on the fly reasoning requires significant search
  deduction within the reactive control loop.

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Towards a Unified Model
Mission Operations Model
Hardware Commanding Failure Model

• Transition Systems Constraints Probabilities

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Fast Search Deep Blue beats Kasparov by brute
force.
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Many problems arent so hard
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General Deduction Can Achieve Reactive Time
Scales
Many problems arent so hard
RISC-like, deductive kernel
4-sat cost
Agenda
TMS
25 var.
FOUND UNSOLVABLE
SOLUTION FOUND
generate successor
conflict database
Average constraints per variable
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Services For Thinking Through Interactions

• Quintuple fault occurs (three shorts, tank-line
  and pressure jacket burst, panel flies off).
• Mattingly works in ground simulator to identify
  new sequence handling severe power limitations.
• Mattingly identifies novel reconfiguration,
  exploiting LEM batteries for power.
• Swaggert Lovell work on Apollo 13 emergency rig
  lithium hydroxide unit.

• Multiple fault diagnosis of unexperienced
  failures.
• Mission planning and scheduling
• Hardware reconfiguration
• Scripted execution

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Remote Agent Architecture
Ground System
RAX_START
RAX_START
Real-Time Execution
RAX Manager
Flight H/W
Fault Monitors
Planning Experts (incl. Navigation)
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Ames-JPL NewMaap New Millennium Advanced
Autonomy Prototype
July - November, 1995
courtesy JPL
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Mission Manager Sets Goals
Thrust Goals
Power
Attitude
Engine
Off
Off
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Plan!
Thrust Goals
Power
Attitude
Thrust (b, 200)
Engine
Off
Warm Up
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Planner Models

• Objects
• state-variables
• tokens
• Constraints
• compatibilities
• functional dependencies

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Compatibility
Thrust Goals
Power
contains
Attitude
Thrust (b, 200)
Engine
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Compatibility
Thrust Goals
Power
equals
contained_by
Point(b)
Attitude
contained_by
meets
met_by
Thrust (b, 200)
Engine
Warm Up
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Planning/Scheduling Cycle
PLAN
NO
Uninstantiated compatibility
. . .
Instantiate compatibility
Heuristics
. . .
Backtrack
Schedule token
NO
YES
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Types of Plan Flaws

• Un-instantiated compatibilities
• subgoaling
• Un-inserted tokens
• Under-constrained parameter
• Gaps in scheduling horizon

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Plan Generates A Simple Temporal Constraint
Network
????????????
????????
????????
?????????
??????
??????
?????????
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DS1 Planner/Scheduler summary

• Model size (Remote Agent Experiment)
• state variables 18
• token literal types 42
• compatibility specs 46
• Plan size
• tokens 154
• temporal relations 180
• variables 288 (81 time points)
• constraints 232 (114 distance bounds)
• Performance
• search nodes 649
• search efficiency 64

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Executing Temporal Plans

• Propagate time
• Select enabled events
• Terminate preceding tokens
• Run next tokens

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Time Propagation Can Be Costly
EXECUTIVE
CONTROLLED SYSTEM
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Compile to Efficient Network
EXECUTIVE
CONTROLLED SYSTEM
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Model-based Execution of Tokens
Programmers and operators must reason through
system-wide interactions to generate codes for

• monitoring
• tracking goals
• confirming commands
• detecting anomalies
• diagnosing faults

• reconfiguring hardware
• coordinating control policies
• recovering from faults
• avoiding failures

Identifying Modes
Reconfiguring Modes
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Model-based Execution asStochastic Optimal
Control
Model
Goals
mode reconfiguration
s(t)
Controller
mode identification
?(t)
o(t)
Plant
s (t)
f
g
Livingstone
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Models

• modes engage physical processes
• probabilistic automata for dynamics

vlvstuck open gt Outflow Mz(inflow)
vlvopen gt Outflow Mz(inflow)
Vlv closed gt Outflow 0
vlvstuck closedgt Outflow 0
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Model-based programming language

• (defcomponent valve (?name)
• attributes ((sign-values (flow (input ?name)))
• (sign-values (flow (output ?name)))
• ...)
• ...
• (closed
• model (and ( (flow (input ?name)) zero)
• ( (flow (output ?name)) zero))
• transitions ((open-valve when
  (open (cmd-in ?name)) next open)
• (otherwise persist)))
• ...)

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Mode Identification and Diagnosis
Observe no thrust
Find most likely reachable states consistent
with observations.
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Mode Reconfiguration
Goal Achieve Thrust
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Models Compile to Propositional Logic

• Specifying Variables
• mode ranges over open, closed, stuck-open,
  stuck-closed
• cmd ranges over open, close, no-cmd
• fin, and fout range over positive, negative,
  zero
• pin, and pout ranging over high, low,
  nominal
• Specifying Mode Behaviors
• mode open ? (pin pout) ? (fin fout)
• mode closed ? (fin zero) ? (fout zero)
• mode stuck-open ? (pin pout) ? (fin fout)
• mode stuck-closed ? (fin zero) ? (fout
  zero)

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• Specifying nominal transitions
• mode closed ? cmd open ? next (mode open)
• mode closed ? cmd ? open ? next (mode closed)
• mode open ? cmd close ? next (mode closed)
• mode open ? cmd ? close ? next (mode open)
• mode stuck-open ? next (mode stuck-open)
• mode stuck-closed ? next (mode stuck-closed)
• Specifying failure transitions
• ?1 mode closed ? next (mode stuck-closed)
• ?2 mode closed ? next (mode stuck-open)
• ?3 mode open ? next (mode stuck-open)
• ?4 mode open ? next (mode stuck-closed)

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Mode identification and reconfiguration performed
by OPSAT
Combinatorial optimization w propositional
constraints A Conflict-directed Search
DPLL TMS
Check Constraints
Best-first Agenda
Optimal feasible modes
DPLL SAT With ITMS
Checked modes
generate best implicants
Conflicts (infeasible modes)
conflict database
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MI and MR performance
Number of components 80 Number of clauses 11101
LTMS
no TMS
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Started January 1996 Launch Fall 1998
courtesy JPL
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Remote Agent Experiment
See rax.arc.nasa.gov

• May 17-18th experiment
• Generate plan for course correction and thrust
• Diagnose camera as stuck on
• Power constraints violated, abort current plan
  and replan
• Perform optical navigation
• Perform ion propulsion thrust
• May 21th experiment.
• Diagnose faulty device and
• Repair by issuing reset.
• Diagnose switch sensor failure.
• Determine harmless, and continue plan.
• Diagnose thruster stuck closed and
• Repair by switching to alternate method of
  thrusting.
• Back to back planning

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Intelligent embedded systems research has
addressed each of AIs concerns

• For reactive systems proving theorems is out
  of the question Agre Chapman 87
• Diagnostic reasoning from a tractable model is
  largely well understood. However we dont know
  how to model complex behavior... Davis
  Hamscher 88
• Commonsense equations are far too general for
  practical use. Sacks Doyle 91

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With autonomy we declare that no sphere is
off limits. We will send our spacecraft to
search beyond the horizon, accepting that we
cannot directly control them, and relying on them
to tell the tale. Bob Rasmussen Architect JPL
Mission Data System