AeroAstro Open House

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• Aero/Astro Open House
• MERS Research Group
• Model-based Embedded and Robotic Systems Group
• Space Systems Laboratory
• Massachusetts Institute of Technology
• Friday, March 30, 2001

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Motivation

• Apollo 13 quintuple fault

• Autonomous systems handle
• Faults
• Anomalies
• Communication
• Commanding

Cooperative Exploration Mars
Outpost Earth Imager
Europa Probe Distant Explorers Mercury Orbiter
Mars Polar Lander failed due to a faulty sensor.
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Model-based Autonomy
Planning
Execution
Monitoring Diagnosis
Reconfiguration
Replanning
Programmers generate functions based on common
sense hardware and software models.
Model-based Programming
Model-based Execution

• Specify common sense, compositional models.
• Models are reusable and easy to articulate at the
  concept stage
• Generative approach covers a broad set of
  behaviors.
• Insensitive to design changes

• Consider as a model-based stochastic, optimal
  controller
• Able to perform significant search and
• deduction within the control loop.
• Fills in details between common sense models
  
• and the environment

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Self-Repairing Explorers

• 1. Flow zero and
• Pressure1 nominal
• 2. Pressure2 nominal
• 3. Acceleration zero

Determine a mode for each component that explains
all observations.
It can not be the case that all valves are open
given the current observations.

• Diagnosis is performed in several steps
• Determine what the conflicting information is
  given sensor information.
• In this case, it is that the Flow zero and
  the Pressure nominal.
• Given this conflicting information, determine a
  set of components that lead to
• the given observations, which are the valves
  in the propulsion system.
• Then, determine the component modes that
  explain all observations.
• In this case, this would be that the
  highlighted valve is closed instead of open.

Gives spacecraft the ability to remain
operational in the face of failures.
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Model-based AutonomousReactive Systems (MARS)

• RMPL models unify planning, diagnosis, learning,
  scheduling and execution representations
• Planner uses high level goals to drive the
  operations of the system.
• Executive will break down the operations from the
  planner into more specific system commands as
  well as monitor the system for failures.

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Model-based Executive (Titan)

• RMPL models (below) compile to Hierarchical
  Constraint Automata
• These automata can be directly executed.

• Only generates non-destructive plans
• Operate on reactive time scales
• Uses precompiled policies to achieve real time
  
• performance

• Mode estimation and reconfiguration rely on
  propositional logic for fast inference.

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Model-Based Execution

• Write control program by asserting and checking
  abstract states (which may be hidden, i.e. not
  directly controllable/observable), rather than
  operating on observable and control variables.
• Underlying Mode Estimation and Reactive Planning
  layer takes care of deducing the spacecraft state
  from the observables, and figuring out how to
  achieve a specified state.

Engine Model
(thrust zero) AND (power_in zero)
Off
standby- cmd
off- cmd
Failed
(thrust zero) AND (power_in nominal)
Standby
0.01
fire- cmd
standby- cmd
0.01
Firing
(thrust full) AND (power_in nominal)
OrbitInsert() (EngineA Standby),
(EngineB Standby), (Camera Off), do
when (EngineA Standby) AND (Camera Off)
donext (EngineA Firing)
watching (EngineA Failed), when (EngineA
Failed) donext when
(EngineB On) AND (Camera Off)
donext (EngineB Firing)

Tell constraint execution layer calls upon the
reactive planning layer to take the appropriate
steps necessary to achieve the specified hidden
state (EngineA Standby) - Turn on Device
Driver A - Issue the standby-cmd
Ask constraint execution layer calls upon the
mode estimation layer to deduce the current
system state, including diagnosis of transition
to (EngineA Failed) - While (EngineA
Firing), IMU data indicates (Thrust zero) - ME
deduces that most likely current state is
(EngineA Failed).
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Real-time Deduction

• Off-line Operations

(valve1 open) and (flow zero) (valve2
open) and not(valve3 open) and (flow zero)
(valve2 stuck closed) and (flow high)
Model
Propositions specify conflicts in system.
RMPL Representation

• On-line Operations

Observations
valve1 open valve2 stuck closed
Propositions specifying conflicts
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MINIature Mode Estimation

• MESSENGER Software Architecture

• Mini-ME embodies fast mode estimation using off
  line operations.

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Distributing the Model-based Paradigm
High-Level Mission Planning

• Code for describing
• Concurrent Task Management
• Resource Management
• Temporal Constraints
• Multiple Method Selection

Distributed Temporal Planner
Distributed Temporal Planner
Distributed Temporal Planner
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Model-based Planner
Model-based Planner
Input Planning models specified as RMPL
models Processing Transforms RMPL models into
Temporal Plan Network (TPN) representation. Ou
tput Temporally-constrained network of events
RMPL
Planning Model
Planner
Path Planning
Plan (i.e Network of Temporally constrained
events)
Plan Runner
success(episode) fail(episode)
episode.start(params) episode.stop(params)
Input Temporally-constrained network of events
from planner. Episode completion or
failure from executive Processing Trim edges
from network Output Sequence of episode start
and stop commands. Start commands Initiate the
episode at specified time Stop commands Force
executive to terminate the episode if executive
has not done so by upper time bounds.
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Path Planning
Before path planning

• The path planner finds a collision free path
  between two points
• Updates the distance between the two points
  (points A and B) accordingly
• Autonomous vehicles follow the path with the best
  cost, in this case the shortest distance

Distance from point A to point B via Path 1
150km
Path 1
or
B
A
Path 2
Distance from point A to point B via Path 2
120km
After path planning
Distance from point A to point B via Path 1
200km
Path 1
B
A
Represents way-points for the vehicles to follow
Distance from point A to point B via Path 2
250km
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Research Extensions Issues

• Hierarchical Plan Graph
• Nodes expand to more complex graph

• Issues
• Plan and re-plan in a distributed manner, while
  being robust to communication losses
• Create a network representation of a plan that
  results in consistent temporal plans
• Develop planning and execution languages that can
  handle real world problems in real-time
• Implement a path planner that quickly finds a
  path from source to destination
• Control heterogeneous teams of cooperative
  robotic explorers

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Monitoring and Diagnosis ofDiscrete/Continuous
Systems

• Modern Physical Artifacts
• composed of interacting hardware and software
• components
• exhibit complex continuous/discrete dynamic
• behavior
• high demands on performance and availability
• autonomous operation without human intervention
• Monitoring and Diagnosis
• track operational mode for supervisory control
• system
• determine health state of the artifact
• provide means for reconfiguration and degraded
• operation to bypass potential catastrophic
  system
• failures

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Motivating Example Mars Polar Lander
Most likely fault Fault of the touchdown sensor
caused a premature engine shutdown at 40 m.
Possible Solution A hybrid monitoring and
diagnosis unit that monitors the continuous
process of descend and the control programs
execution would have been able to detect such a
subtle fault and prevent the engine shutdown.
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Hybrid Modeling of combined hardware/software
system
Hybrid Probabilistic Automata model M set of
discrete modes x, u, y continuous state,
input, output F continuous dynamics for each
mode (ODE) T probabilistic transitions
between modes
Hybrid Probabilistic Automata (HPA) is a hidden
Markov model, encoded as a set of modes that
exhibit a continuous dynamical behavior, which
are expressed by differential or difference
equations.
E.g. probabilistic transitions system
transitions either to a new nominal mode or to a
failure mode

• Research Issues
• Composition of HPA component models -gt
  Concurrent Hybrid Probabilistic Automata
• RMPL unified compositional language for
  systems modeling allows to describe complex
  hardware, software
• components and their system-wide interaction
  -gt compiles to Concurrent Hybrid Probabilistic
  Automata

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Hybrid Mode Estimation and Diagnosis
Hybrid State Estimator
Hybrid State Estimator maintains a set of likely
hybrid state estimates
Hybrid mode estimator determines for each
trajectory the possible transitions, and
specifies the candidate trajectories to be
tracked by the continuous state estimators.

• Research Issues
• Exponential explosion of possible hybrid state
  estimates -gt focus on the most likely one by
  applying AI techniques
• currently formulated for HPA extension to
  concurrent HPA models
• Real-time operation anytime algorithm

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Learning the System Model

• Our Knowledge about the system might be
  incomplete
• how do we learn the parameters of the concurrent
  PHA?
• 1. F parameter estimation for the nonlinear
• ODE system that governs the continuous
  behavior
• -gt parameter estimation for nonlinear
  dynamic systems
• 2. T estimate the transition probabilities
• -gt parameter estimation for hidden Markov
  models

7 parameters, 9 sensors

• Research Issues
• Decompose models, select applicable data,
  coordinate and combine estimates.
• How do we converge quickly with sparse data and
  avoid getting stuck in local minima?
• How do we generalize automated decomposition and
  simplification methods to hybrid automata?
• Is there a unification of mode identification
  and estimation that exploits continuous and
  discrete,
• compile time decomposition?

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Testbeds
Rover Testbed
The Model-based Embedded and Robotics Systems
group maintains a facility to evaluate various
aspects of rover control using one ATRV and three
ATRV-Jr. Rovers exhibit a rich set of continuous
and discrete behavior and therefore will serve as
the major testbed for evaluating and validating
our proposed monitoring, diagnosis, model
learning and refinement methods. Mode estimation
will involve determining the rovers internals
state and its state with respect to its
surrounding world. We will provide a rich set of
sensory inputs, such as load sensors for
actuators, tactile sensors, sonars, etc. to
allow a comprehensive mode estimation. Evaluation
of the proposed hybrid health management
techniques will also include mode estimation of
on-board scientific instruments, such as an
spectroscopic sensor.
Advanced Live Support System - BIOPlex
Previously funded research at JSC has led to a
variety of advanced live support (ALS) testbeds.
We will apply the hybrid mode estimation
techniques to the ALS system dynamic simulation
testbed which provides a simple, integrated
simulation of a BIO-Plex-like ALS system. The
simulation models energy, water recovery, air
revitalization, plant growth and crew activities.
This example will challenge our approach by
describing a highly complex mechanism consisting
of tightly coupled sub-systems, a large number of
sensors and actuators, and the interaction with
crew members living in this artificial habitat.
This testbed will also serve as the major
evaluation platform for our model learning
techniques.