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Topics: Introduction to Robotics CS 491/691(X) Lecture 2 Instructor: Monica Nicolescu Review Definitions Robots, robotics Robot components Sensors, actuators, control State, state space Representation Spectrum of robot control Reactive, deliberative Robot Control

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topics introduction to robotics cs 491 691 x

Topics: Introduction to RoboticsCS 491/691(X)

Lecture 2

Instructor: Monica Nicolescu

review
Review
  • Definitions
    • Robots, robotics
  • Robot components
    • Sensors, actuators, control
  • State, state space
  • Representation
  • Spectrum of robot control
    • Reactive, deliberative

CS 491/691(X) - Lecture 2

robot control
Robot Control
  • Robot control is the means by which the sensing and action of a robot are coordinated
  • The infinitely many possible robot control programs all fall along a well-defined control spectrum
  • The spectrum ranges from reacting to deliberating

CS 491/691(X) - Lecture 2

spectrum of robot control
Spectrum of robot control

From “Behavior-Based Robotics” by R. Arkin, MIT Press, 1998

CS 491/691(X) - Lecture 2

robot control approaches
Robot control approaches
  • Reactive Control
    • Don’t think, (re)act.
  • Deliberative (Planner-based) Control
    • Think hard, act later.
  • Hybrid Control
    • Think and act separately & concurrently.
  • Behavior-Based Control (BBC)
    • Think the way you act.

CS 491/691(X) - Lecture 2

reactive control don t think react
Technique for tightly coupling perception and action to provide fast responses to changing, unstructured environments

Collection of stimulus-response rules

Limitations

No/minimal state

No memory

No internal representations

of the world

Unable to plan ahead

Unable to learn

Advantages

Very fast and reactive

Powerful method: animals are largely reactive

Reactive Control:Don’t think, react!

CS 491/691(X) - Lecture 2

deliberative control think hard then act
Deliberative Control: Think hard, then act!
  • In DC the robot uses all the available sensory information and stored internal knowledge to create a plan of action: sense  plan  act (SPA) paradigm
  • Limitations
    • Planning requires search through potentially all possible plans  these take a long time
    • Requires a world model, which may become outdated
    • Too slow for real-time response
  • Advantages
    • Capable of learning and prediction
    • Finds strategic solutions

CS 491/691(X) - Lecture 2

hybrid control think and act independently concurrently
Hybrid Control: Think and act independently & concurrently!
  • Combination of reactive and deliberative control
    • Reactive layer (bottom): deals with immediate reaction
    • Deliberative layer (top): creates plans
    • Middle layer: connects the two layers
  • Usually called “three-layer systems”
  • Major challenge: design of the middle layer
    • Reactive and deliberative layers operate on very different time-scales and representations (signals vs. symbols)
    • These layers must operate concurrently
  • Currently one of the two dominant control paradigms in robotics

CS 491/691(X) - Lecture 2

behavior based control think the way you act
Behavior-Based Control:Think the way you act!
  • An alternative to hybrid control, inspired from biology
  • Has the same capabilities as hybrid control:
    • Act reactively and deliberatively
  • Also built from layers
    • However, there is no intermediate layer
    • Components have a uniform representation and time-scale
    • Behaviors: concurrent processes that take inputs from sensors and other behaviors and send outputs to a robot’s actuators or other behaviors

CS 491/691(X) - Lecture 2

behavior based control think the way you act10
Behavior-Based Control:Think the way you act!
  • “Thinking” is performed through a network of behaviors
  • Utilize distributed representations
  • Respond in real-time
    • are reactive
  • Are not stateless
    • not merely reactive
  • Allow for a variety of behavior coordination mechanisms

CS 491/691(X) - Lecture 2

fundamental differences of control
Fundamental Differences of Control
  • Time-scale: How fast do things happen?
    • how quickly the robot has to respond to the environment, compared to how quickly it can sense and think
  • Modularity: What are the components for control?
    • Refers to the way the control system is broken up into modules and how they interact with each other
  • Representation: What does the robot keep in its brain?
    • The form in which information is stored or encoded in the robot

CS 491/691(X) - Lecture 2

a brief history of robotics
A Brief History of Robotics
  • Robotics grew out of the fields of control theory, cyberneticsandAI
  • Robotics, in the modern sense, can be considered to have started around the time of cybernetics (1940s)
  • Early AI had a strong impact on how it evolved (1950s-1970s), emphasizing reasoning and abstraction, removal from direct situatedness and embodiment
  • In the 1980s a new set of methods was introduced and robots were put back into the physical world

CS 491/691(X) - Lecture 2

control theory
Control Theory
  • The mathematical study of the properties of automated control systems
    • Helps understand the fundamental concepts governing all mechanical systems (steam engines, aeroplanes, etc.)
    • Relies on the idea of feedback control
  • Thought to have originated with the ancient greeks
    • Time measuring devices (water clocks), water systems
  • Forgotten and rediscovered in Renaissance Europe
    • Heat-regulated furnaces (Drebbel, Reaumur, Bonnemain)
    • Windmills
  • James Watt’s steam engine (the governor)

CS 491/691(X) - Lecture 2

feedback control
Feedback Control
  • Definition:technique for bringing and maintaining a system in a goal state, as the external conditions vary
  • Idea: continuously feeding back the current state and comparing it to the desired state, then adjusting the current state to minimize the difference (negative feedback).
    • The system is said to be self-regulating
  • E.g.: thermostats
    • if too hot, turn down, if too cold, turn up

CS 491/691(X) - Lecture 2

cybernetics
Cybernetics
  • Pioneered by Norbert Wiener in the 1940s
    • Comes from the Greek word “kibernts” – governor, steersman
  • Combines principles of control theory, information science and biology
  • Sought principles common to animals and machines, especially with regards to control and communication
  • Studied the coupling between an organism and its environment

CS 491/691(X) - Lecture 2

w grey walter s tortoise
W. Grey Walter’s Tortoise
  • Machina Speculatrix” (1953)
    • 1 photocell, 1 bump sensor, 1 motor, 3 wheels, 1 battery
  • Behaviors:
    • seek light
    • head toward moderate light
    • back from bright light
    • turn and push
    • recharge battery
  • Uses reactive control, with behavior prioritization

CS 491/691(X) - Lecture 2

principles of walter s tortoise
Principles of Walter’s Tortoise
  • Parsimony
    • Simple is better
  • Exploration or speculation
    • Never stay still, except when feeding (i.e., recharging)
  • Attraction (positive tropism)
    • Motivation to move toward some object (light source)
  • Aversion (negative tropism)
    • Avoidance of negative stimuli (heavy obstacles, slopes)
  • Discernment
    • Distinguish between productive/unproductive behavior (adaptation)

CS 491/691(X) - Lecture 2

braitenberg vehicles
Braitenberg Vehicles
  • Valentino Braitenberg (1980)
  • Thought experiments
    • Use direct coupling between sensors and motors
    • Simple robots (“vehicles”) produce complex behaviors that appear very animal, life-like
  • Excitatory connection
    • The stronger the sensory input, the stronger the motor output
    • Light sensor  wheel: photophilic robot (loves the light)
  • Inhibitory connection
    • The stronger the sensory input, the weaker the motor output
    • Light sensor  wheel: photophobic robot (afraid of the light)

CS 491/691(X) - Lecture 2

example vehicles
Example Vehicles
  • Wide range of vehicles can be designed, by changing the connections and their strength
  • Vehicle 1:
    • One motor, one sensor
  • Vehicle 2:
    • Two motors, two sensors
    • Excitatory connections
  • Vehicle 3:
    • Two motors, two sensors
    • Inhibitory connections

Vehicle 1

Being “ALIVE”

“FEAR” and “AGGRESSION”

Vehicle 2

“LOVE”

CS 491/691(X) - Lecture 2

artificial intelligence
Artificial Intelligence
  • Officially born in 1956 at Dartmouth University
    • Marvin Minsky, John McCarthy, Herbert Simon
  • Intelligence in machines
    • Internal models of the world
    • Search through possible solutions
    • Plan to solve problems
    • Symbolic representation of information
    • Hierarchical system organization
    • Sequential program execution

CS 491/691(X) - Lecture 2

ai and robotics
AI and Robotics
  • AI influence to robotics:
    • Knowledge and knowledge representation are central to intelligence
  • Perception and action are more central to robotics
  • New solutions developed: behavior-based systems
    • “Planning is just a way of avoiding figuring out what to do next” (Rodney Brooks, 1987)
  • Distributed AI (DAI)
    • Society of Mind (Marvin Minsky, 1986): simple, multiple agents can generate highly complex intelligence
  • First robots were mostly influenced by AI (deliberative)

CS 491/691(X) - Lecture 2

shakey
Shakey
  • At Stanford Research Institute (late 1960s)
  • A deliberative system
  • Visual navigation in a very special world
  • STRIPS planner
  • Vision and contact sensors

CS 491/691(X) - Lecture 2

early ai robots hilare
Early AI Robots: HILARE
  • Late 1970s
  • At LAAS in Toulouse
  • Video, ultrasound, laser rangefinder
  • Was in use for almost 2 decades
  • One of the earliest hybrid architectures
  • Multi-level spatial representations

CS 491/691(X) - Lecture 2

early robots cart rover
Early Robots: CART/Rover
  • Hans Moravec’s early robots
  • Stanford Cart (1977) followed by CMU rover (1983)
  • Sonar and vision

CS 491/691(X) - Lecture 2

lessons learned
Lessons Learned
  • Move faster
  • Think in such a way as to allow this action
  • New types of robot control:
    • Reactive, hybrid, behavior-based
  • Control theory
    • Continues to thrive in numerous applications
  • Cybernetics
    • Biologically inspired robot control
  • AI
    • Non-physical, “disembodied thinking”

CS 491/691(X) - Lecture 2

challenges
Challenges
  • Perception
    • Limited, noisy sensors
  • Actuation
    • Limited effectors
  • Thinking
    • Time consuming in large state spaces
  • Dynamic environments
    • Impose fast reaction times

CS 491/691(X) - Lecture 2

key issues of behavior based control
Key Issues of Behavior-Based Control
  • Situatedness:
    • Robot is entirely situated in the real world
  • Embodiment:
    • Robot has a physical body
  • Emergence:
    • Intelligence from the interaction with the environment
  • Grounding in reality
    • Correlation with the reality
  • Scalability
    • Reaching high-level intelligence

CS 491/691(X) - Lecture 2

effectors actuators
Effectors & Actuators
  • Effector
    • Any device robot that has an impact on the environment
    • Effectors must match a robot’s task
    • Controllers command the effectors to achieve the desired task
  • Actuator
    • A robot mechanism that enables the effector to execute an action
  • Robot effectors are very different than biological ones
    • Robots: wheels, tracks, grippers
  • Robot actuators:
    • Electric motors, hydraulic, pneumatic cylinders, temperature-sensitive materials

CS 491/691(X) - Lecture 2

actuation
Actuation
  • Passive actuation
    • Use potential energy and interaction with the environment
  • E.g.: gliding (flying squirrels)
  • Robotics examples:
    • Tad McGeer’s passive walker
    • Actuated by gravity

CS 491/691(X) - Lecture 2

types of actuators
Types of Actuators
  • Electric motors
  • Hydraulics
  • Pneumatics
  • Photo-reactive materials
  • Chemically reactive materials
  • Thermally reactive materials
  • Piezoelectric materials

CS 491/691(X) - Lecture 2

dc motors
DC Motors
  • DC (direct current) motors
    • Convert electrical energy into mechanical energy
    • Small, cheap, reasonably efficient, easy to use
  • How do they work?
    • Electrical current through loops of wires mounted on a rotating shaft
    • When current is flowing, loops of wire generate a magnetic field, which reacts against the magnetic fields of permanent magnets positioned around the wire loops
    • These magnetic fields push against one another and the armature turns

CS 491/691(X) - Lecture 2

motor efficiency
Motor Efficiency
  • DC motors are not perfectly efficient
  • Some limitations (mechanical friction) of motors
    • Some energy is wasted as heat
  • Industrial-grade motors (good quality): 90%
  • Toy motors (cheap): efficiencies of 50%
  • Electrostatic micro-motors for miniature robots: 50%

CS 491/691(X) - Lecture 2

operating voltage
Operating Voltage
  • Making the motor run requires electrical power in the right voltage range
  • Most motors will run fine at lower voltages, though they will be less powerful
  • Can operate at higher voltages at expense of operating life

CS 491/691(X) - Lecture 2

operating stall current
Operating/Stall Current
  • When provided with a constant voltage, a DC motor draws current proportional to how much work it is doing
  • When there is no resistance to its motion, the motor draws the least amount of current
    • Moving in free space  less current
    • Pushing against an obstacle  drain more current
  • If the resistance becomes very high the motor stalls and draws the maximum amount of current at its specified voltage (stall current)

CS 491/691(X) - Lecture 2

torque
Torque
  • Torque: rotational force that a motor can deliver at a certain distance from the shaft
  • The more current through a motor, the more torque at the motor’s shaft
  • Strength of magnetic field generated in loops of wire is directly proportional to amount of current flowing through them and thus the torque produced on motor’s shaft

CS 491/691(X) - Lecture 2

stall torque
Stall Torque
  • Stall torque:the amount of rotational force produced when the motor is stalled at its recommended operating voltage, drawing the maximal stall current at this voltage
  • Typical torque units: ounce-inches
    • 5 oz.-in. torque means motor can pull weight of 5 oz up through a pulley 1 inch away from the shaft

CS 491/691(X) - Lecture 2

power of a motor
Power of a Motor
  • Power: product of the output

shaft’s rotational velocity and

torque

  • No load on the shaft
    • Rotational velocity is at its highest, but the torque is zero
    • The motor is spinning freely (it is not driving any mechanism)
  • Motor is stalled
    • It is producing its maximal torque
    • Rotational velocity is zero

A motor produces the most power in the middle of its performance range.

CS 491/691(X) - Lecture 2

how fast do motors turn
How Fast do Motors Turn?
  • Free spinning speeds (most motors):
    • 3000-9000 RPM (revolutions per minute) [50-150 RPS]
  • High-speed, low torque
    • Drive light things that rotate very fast
  • What about driving a heavy robot body or lifting a heavy manipulator?
    • Need more torque and less speed

CS 491/691(X) - Lecture 2

readings
Readings
  • F. Martin: Section 4.1
  • M. Matarić: Chapters 2, 4

CS 491/691(X) - Lecture 2