16.412j/6.835 intelligent embedded systems
DESCRIPTION
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. Outline. Course Objectives and Assignments Types of Reasoning Kinds of Intelligent Embedded Systems A Case Study: Space Explorers. Plan. Monitor & Diagnosis. - PowerPoint PPT PresentationTRANSCRIPT
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16.412J/6.835 Intelligent Embedded Systems
Prof. Brian Williams
Rm 37-381
Rm NE43-838
MW 11-12:30, Rm 33-418
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OutlineOutline
• Course Objectives and Assignments
• Types of Reasoning
• Kinds of Intelligent Embedded Systems
• A Case Study: Space Explorers
• Course Objectives and Assignments
• Types of Reasoning
• Kinds of Intelligent Embedded Systems
• A Case Study: Space Explorers
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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
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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
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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.
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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
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OutlineOutline
• Course Objectives and Assignments• Types of Reasoning
(Slides compliments of Prof Malik, Berkeley)
• Kinds of Intelligent Embedded Systems
• A Case Study: Space Explorers
• Course Objectives and Assignments• Types of Reasoning
(Slides compliments of Prof Malik, Berkeley)
• Kinds of Intelligent Embedded Systems
• A Case Study: Space Explorers
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Agents and IntelligenceAgents and Intelligence
Prof Malik, Berkeley
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Reflex agentsReflex agents
Compliments of Prof Malik, Berkeley
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Reflex agent with stateReflex agent with state
Compliments of Prof Malik, Berkeley
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Goal-oriented agentGoal-oriented agent
Compliments of Prof Malik, Berkeley
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Utility-based agentUtility-based agent
Compliments of Prof Malik, Berkeley
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OutlineOutline
• Course Objectives and Assignments
• Types of Reasoning
• Kinds of Intelligent Embedded Systems
• A Case Study: Space Explorers
• Course Objectives and Assignments
• Types of Reasoning
• Kinds of Intelligent Embedded Systems
• A Case Study: Space Explorers
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Immobile Robots: Intelligent Offices and Ubiquitous Computing
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Ecological Life SupportFor Mars Exploration
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courtesy NASA
The MIR Failure
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courtesy NASA Ames
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MIT Spherescourtesy Prof. Dave Miller, MIT Space Systems Laboratory
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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
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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
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Cooperative Exploration
Distributed Planning Group, JPLModel-based Embedded
and Robotic Systems Group, MIT
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MIT Model Based Embedded and Robotics Group
Autonomous Vehicles Testbed
MIT Model Based Embedded and Robotics Group
Autonomous Vehicles Testbed
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Robotic VehiclesRobotic Vehicles
• ATRV Rovers• Monster Trucks• Blimps• Spheres
• Simulated Air/Space Vehicles
• ATRV Rovers• Monster Trucks• Blimps• Spheres
• Simulated Air/Space Vehicles
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Indoor test rangeIndoor test range
Aim & Scope:• indoor experiments for
target site exploration• cooperative exploration
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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 …
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Cryobot & Hydrobot courtesy JPL
Exploring life under EuropaExploring life under Europa
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OutlineOutline
• Course Objectives and Assignments
• Types of Reasoning
• Kinds of Intelligent Embedded Systems
• A Case Study: Space Explorers
• Course Objectives and Assignments
• Types of Reasoning
• Kinds of Intelligent Embedded Systems
• A Case Study: Space Explorers
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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
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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
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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
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Vanished:• Mars Polar Lander• Mars Observer
courtesy of JPL
Spacecraft require commonsense…
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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;
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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.
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Self Repairing Explorers: Deep Space 1