david vernon short intro to robotics and ai
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Copyright 2007 David Vernon (www.vernon.eu)
A Short Introduction to
Robotics and AI
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Learning Objectives
Nature of robotics
Robotic applications
Principal engineering issues
Principal AI issues The future of robotics
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The Word Robot
Robot is derived from a Czech word meaningforced labor
First appeared in a 1920 play R.U.R.(Rossums Universal Robots)
by Czech playwright Karel Capek.
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Definitions
"A reprogrammable, multifunctional manipulator designedto move material, parts, tools, or specialized devicesthrough various programmed motions for theperformance of a variety of tasks
Robot Institute of America
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Types of Robot
Manipulators
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Unimate Puma Robot
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Adept's SCARA robots,images courtesy ofAdept Technology
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Stubli's RX130manipulators, images
courtesy of Stubli
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CRS' F3 robot testing amobile phone, image
courtesy of CRS
Robotics
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BH8-260 Hand from Barrett Technology,
image courtesy of Barrett
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The Space Station Remote Manipulator System (SSRMS)
Canadarm - Robot arm on every Space Shuttle, image courtesy of
the Canadian Space Agency
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Types of Robot
Manipulators
Mobile robots
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Urban Robot Platform - image courtesy of
NASA JPL
Urban Robot Platform - image courtesy of NASA JPL
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iRobot-LE - imagecourtesy of iRobot
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Oberon - underwater robot developed by the
Australian Centre for Field Robotics
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Dante II robot, imagecourtesy of NASA
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Mars Sojourner, image courtesy of NASA
Urban Robot Platform - image courtesy of NASA JPL
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Mars Sojourner, image courtesy of NASA
Urban Robot Platform - image courtesy of NASA JPL
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Honda's P1 HumanoidRobot
(controlled via tether)
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Honda's P3 Robot
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Honda's ASIMO Robot
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Honda's ASIMO Robot
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Types of Robot
Manipulators
Mobile robots
Entertainment
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Sony's 1st Generation
Aibo Robot Dog
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MIT AI Lab COG
(with Rodney Brooks)
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iCub
(www.iCub.org)
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Robotic Applications Parts handling
Assembly Painting
Surveillance
Security (bomb disposal really telechericsrather than robotics)
Home help (grass cutting, nursing)
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CMU Nursebot project
FLO
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Exploration
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Mechanical Construction
ControllerArm
Drive
End Effectors
SensorPlease click over the picture
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Trajectory Generation
Cartesian space vs. Joint Space
Manipulator kinematics (given joint angles, find positionand orientation of end effector)
Inverse kinematics (given position and orientation ofend effector, find joint angles)
Dynamics
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Trajectory Generation
Problems with Cartesian space interpolation
Unreachable configurations
Multiple solutions
Task Specification & World
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as Spec cat o & o d
Modelling
Specification of orientation:
Use transformation matrices and Eulerangles or Roll-Pitch-Yaw convention
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AI & Robotics
Interaction with the environment
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The Real World
Uncertain, Incomplete, Time-varying
From Introduction to AI Robotics, Robin Murphy
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AI &Robotics
Agent based examples taken from:Q. Tipu, University of Southern California
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AI & Robotics How to represent knowledge about the world?
How to react to new perceived events? How to integrate new percepts to past experience?
How to understand the user?
How to optimize balance between user goals & environment constraints? How to use reasoning to decide on the best course of action?
How to communicate back with the user?
How to plan ahead? How to learn from experience?
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Agent Types
We can split agent research into two main strands:
Distributed Artificial Intelligence (DAI) Multi-Agent Systems (MAS) (1980 1990)
Much broader notion of "agent"(1990s present) interface, reactive, mobile, information
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Towards Autonomous Vehicles
http://iLab.usc.edu
http://beobots.org
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Interacting Agents
Lane Keeping Agent (LKA) Goals: Stay in current lane
Percepts: Lane center, lane boundaries
Sensors: Vision Effectors: Steering Wheel, Accelerator, Brakes
Actions: Steer, speed up, brake
Environment: Freeway
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Interacting Agents
Collision Avoidance Agent (CAA) Goals: Avoid running into obstacles
Percepts: Obstacle distance, velocity, trajectory
Sensors: Vision, proximity sensing
Effectors: Steering Wheel, Accelerator, Brakes, Horn, Headlights
Actions: Steer, speed up, brake, blow horn, signal (headlights)
Environment: Freeway
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The Right Thing =
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The Rational Action Rational Action: The action that maximizes the
expected value of the performance measure given
the percept sequence to date
Rational = Best ?
Rational = Optimal ? Rational = Omniscience ?
Rational = Clairvoyant ?
Rational = Successful ?
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Behavior and performance of IAs
Perception (sequence) to Action Mapping: f: P* A
Ideal mapping: specifies which actions an agent ought to take at anypoint in time
Description: Look-Up-Table vs. Closed Form
Performance measure: a subjectivemeasure to characterize how
successful an agent is (e.g., speed, power usage, accuracy, money,etc.)
(degree of) Autonomy: to what extent is the agent able to make
decisions and actions on its own?
How is an Agent different from other
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software?
Agents are autonomous, that is they act on behalf of the user Agents contain some level of intelligence, from fixed rules to learning
engines that allow them to adapt to changes in the environment
Agents don't only act reactively, but sometimes also proactively
Agents have social ability, that is they communicate with the user, thesystem, and other agents as required
Agents may also cooperate with other agents to carry out more complextasks than they themselves can handle
Agents may migrate from one system to another to access remote
resources or even to meet other agents
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Agent types Reflex agents
Reflex agents with internal states Goal-based agents
Utility-based agents
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Reflex agents
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Reflex agents w/ state
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Goal-based agents
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Utility-based agents
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Anticipate
AssimilateAdapt
Action Perception
Cognition
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simulated sensory signals
Perturbation
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Phylogenetic self-organizingperceptuo-motor skills
Modulation circuit:homeostatic action selection
by disinhibition of perceptuo-motor skills
Motivation(Amygdala)
Auto-associativeMemory
(Hippocampus)
ActionSelection
(Basal Ganglia)
Motor/SensoryAuto-associative
Memory
Sensory/MotorAuto-associative
Memory
simulated motor signals
Prospection by
action simulation
Perturbation
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