16.412J/6.835 Intelligent Embedded Systems Prof. Brian Williams Rm 37-381 Rm NE43-838...

16.412J/6.835 Intelligent Embedded Systems

Prof. Brian Williams

Rm 37-381

Rm NE43-838

[email protected]

MW 11-12:30, Rm 33-418

OutlineOutline

• Course Objectives and Assignments

• Types of Reasoning

• Kinds of Intelligent Embedded Systems

• A Case Study: Space Explorers





Course Objective 1Course Objective 1• To understand fundamental methods for creating the

major components of intelligent embedded systems.

Accomplished by: First ten lectures on basic methods ~ 5 problem sets during the first ten lectures to exercise

basic understanding of methods.

• To understand fundamental methods for creating the major components of intelligent embedded systems.

Accomplished by: First ten lectures on basic methods ~ 5 problem sets during the first ten lectures to exercise

basic understanding of methods.

Plan

ExecuteMonitor &Diagnosis

Basic Method LecturesBasic Method Lectures• Decision Theoretic Planning• Reinforcement Learning• Partial Order Planning• Conditional Planning and Plan Execution• Propositional Logic and Inference• Model-based Diagnosis• Temporal Planning and Execution• Bayesian Inference and Learning

More Advanced:• Graph-based and Model-based Planning• Combining Hidden Markov Models and Symbolic Reasoning

• Decision Theoretic Planning• Reinforcement Learning• Partial Order Planning• Conditional Planning and Plan Execution• Propositional Logic and Inference• Model-based Diagnosis• Temporal Planning and Execution• Bayesian Inference and Learning

More Advanced:• Graph-based and Model-based Planning• Combining Hidden Markov Models and Symbolic Reasoning

Course Objective 2Course Objective 2

• To dive into the recent literature, and collectively synthesize, clearly explain and evaluate the state of the art in intelligent embedded systems.

Accomplished by: Weekly thought questions (~ 2 page answers) Group lecture on advance topic

45 minute lecture Short tutorial article on method 1-3 methods Demo of example reasoning algorithm Groups of size ~3.

• To dive into the recent literature, and collectively synthesize, clearly explain and evaluate the state of the art in intelligent embedded systems.

Accomplished by: Weekly thought questions (~ 2 page answers) Group lecture on advance topic

45 minute lecture Short tutorial article on method 1-3 methods Demo of example reasoning algorithm Groups of size ~3.

Course Objective 3Course Objective 3

• To apply one or more reasoning elements to create a simple agent that is driven by Goals or Rewards

Accomplished by: Final project during last third of course

Implement and demonstrate one or more reasoning methods on a simple embedded system.

Short final presentation on project. Final project report.

• To apply one or more reasoning elements to create a simple agent that is driven by Goals or Rewards

Accomplished by: Final project during last third of course

Implement and demonstrate one or more reasoning methods on a simple embedded system.

Short final presentation on project. Final project report.

Plan

ExecuteMonitor &Diagnosis

OutlineOutline

• Course Objectives and Assignments• Types of Reasoning

(Slides compliments of Prof Malik, Berkeley)



• Course Objectives and Assignments• Types of Reasoning

(Slides compliments of Prof Malik, Berkeley)



Agents and IntelligenceAgents and Intelligence

Prof Malik, Berkeley

Reflex agentsReflex agents

Compliments of Prof Malik, Berkeley

Reflex agent with stateReflex agent with state


Goal-oriented agentGoal-oriented agent


Utility-based agentUtility-based agent


OutlineOutline









Immobile Robots: Intelligent Offices and Ubiquitous Computing

Ecological Life SupportFor Mars Exploration

courtesy NASA

The MIR Failure

courtesy NASA Ames

MIT Spherescourtesy Prof. Dave Miller, MIT Space Systems Laboratory

courtesy JPL

Distributed Spacecraft Interferometers to Distributed Spacecraft Interferometers to search for Earth-like Planets Around Other Starssearch for Earth-like Planets Around Other Stars

courtesy JPL

``Our vision in NASA is to open the Space Frontier . . . We must establish a virtual presence, in space, on planets, in aircraft and spacecraft.’’

- Daniel S. Goldin, NASA Administrator, May 29, 1996

A Goldin Era of Robotic Space Exploration

Cooperative Exploration

Distributed Planning Group, JPLModel-based Embedded

and Robotic Systems Group, MIT

MIT Model Based Embedded and Robotics Group

Autonomous Vehicles Testbed

MIT Model Based Embedded and Robotics Group

Autonomous Vehicles Testbed

Robotic VehiclesRobotic Vehicles

• ATRV Rovers• Monster Trucks• Blimps• Spheres

• Simulated Air/Space Vehicles

• ATRV Rovers• Monster Trucks• Blimps• Spheres

• Simulated Air/Space Vehicles

Indoor test rangeIndoor test range

Aim & Scope:• indoor experiments for

target site exploration• cooperative exploration

ScenarioScenarioCooperative Target Site Exploration:

Heterogeneous rover team and blimps explore science sites determined by remote sensing

exploration featurepath planned/takenway point

exploration regionidentified featuregoal position

Tasks:• small scout rovers (ATRV Jr)

explore terrain as described in earlier scenarios

• blimps provide additional fine grain air surveillance

• scout rovers identify features for further investigation by sample rover (ATRV)

• scout rovers provide refined terrain mapping for path planning of the larger sample rover

Scenario Research Objective• Extend coordination to

heterogeneous team …

Cryobot & Hydrobot courtesy JPL

Exploring life under EuropaExploring life under Europa

OutlineOutline









Cassini Maps Titan courtesy JPL

• 7 year cruise

• ~ 150 - 300 ground operators

•~ 1 billion $

• 7 years to build

A Capable Robotic Explorer: Cassini

•150 million $

•2 year build

• 0 ground ops

Faster, Better, Cheaper

courtesy JPL

``Our vision in NASA is to open the Space Frontier . . . We must establish a virtual presence, in space, on planets, in aircraft and spacecraft.’’

- Daniel S. Goldin, NASA Administrator, May 29, 1996

Four launches in 7 months

Mars Climate Orbiter: 12/11/98Mars Polar Lander: 1/3/99

Stardust: 2/7/99 QuickSCAT: 6/19/98courtesy of JPL

Vanished:• Mars Polar Lander• Mars Observer

courtesy of JPL

Spacecraft require commonsense…

Traditional spacecraft commanding

GS,SITURN,490UA,BOTH,96-355/03:42:00.000; CMD,7GYON, 490UA412A4A,BOTH, 96-355/03:47:00:000, ON; CMD,7MODE, 490UA412A4B,BOTH, 96-355/03:47:02:000, INT; CMD,6SVPM, 490UA412A6A,BOTH, 96-355/03:48:30:000, 2; CMD,7ALRT, 490UA412A4C,BOTH, 96-355/03:50:32:000, 6; CMD,7SAFE, 490UA412A4D,BOTH, 96-355/03:52:00:000, UNSTOW; CMD,6ASSAN,490UA412A6B,BOTH, 96-355/03:56:08:000, GV,153,IMM,231,

GV,153; CMD,7VECT, 490UA412A4E,BOTH, 96-355/03:56:10.000, 0,191.5,6.5,

0.0,0.0,0.0,96-350/00:00:00.000,MVR;

SEB,SCTEST,490UA412A23A,BOTH, 96-355/03:56:12.000, SYS1,NPERR; CMD,7TURN, 490UA412A4F,BOTH, 96-355/03:56:14.000, 1,MVR; MISC,NOTE, 490UA412A99A,, 96-355/04:00:00.000, ,START OF TURN;, CMD,7STAR, 490UA412A406A4A,BOTH 96-355/04:00:02.000, 7,1701,

278.813999,38.74; CMD,7STAR, 490UA412A406A4B,BOTH,96-355/04:00:04.000, 8,350,120.455999,

-39.8612; CMD,7STAR, 490UA412A406A4C,BOTH,96-355/04:00:06.000, 9,875,114.162,

5.341; CMD,7STAR, 490UA412A406A4D,BOTH,96-355/04:00:08.000, 10,159,27.239,

89.028999; CMD,7STAR, 490UA412A406A4E,BOTH,96-355/04:00:10.000, 11,0,0.0,0.0; CMD,7STAR, 490UA412A406A4F,BOTH,96-355/04:00:12.000, 21,0,0.0,0.0;

Houston, We have a problem ...

courtesy of NASA

• 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.

Self Repairing Explorers: Deep Space 1

16.412J/6.835 Intelligent Embedded Systems Prof. Brian Williams Rm 37-381 Rm NE43-838...

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