1. Topics: Introduction to Robotics CS 491/691(X)
Lecture 2
Instructor: Monica Nicolescu

2. Review Definitions Robot components State, state space Representation
Robots, robotics
Robot components
Sensors, actuators, control
State, state space
Representation
Spectrum of robot control
Reactive, deliberative
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3. 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
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4. Spectrum of robot control
From “Behavior-Based Robotics” by R. Arkin, MIT Press, 1998
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5. 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.
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6. 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
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7. 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Requires a world model, which may become outdated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
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8. 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
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9. 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
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10. 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
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11. 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
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12. A Brief History of Robotics
Robotics grew out of the fields of control theory, cybernetics and AI
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
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13. 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)
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14. 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
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15. 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
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16. 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
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17. 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)
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18. 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)
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19. 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:
Inhibitory connections
Vehicle 1
Being “ALIVE”
“FEAR” and “AGGRESSION”
Vehicle 2
“LOVE”
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20. 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
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21. 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)
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22. Shakey At Stanford Research Institute (late 1960s)
A deliberative system
Visual navigation in a very special world
STRIPS planner
Vision and contact sensors
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23. 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
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24. Early Robots: CART/Rover
Hans Moravec’s early robots
Stanford Cart (1977) followed by CMU rover (1983)
Sonar and vision
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25. 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”
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26. Challenges Perception Actuation Thinking Dynamic environments
Limited, noisy sensors
Actuation
Limited effectors
Thinking
Time consuming in large state spaces
Dynamic environments
Impose fast reaction times
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27. 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
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28. Effectors & Actuators Effector Actuator
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
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29. Actuation Passive actuation E.g.: gliding (flying squirrels)
Use potential energy and interaction with the environment
E.g.: gliding (flying squirrels)
Robotics examples:
Tad McGeer’s passive walker
Actuated by gravity
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30. Types of Actuators Electric motors Hydraulics Pneumatics
Photo-reactive materials
Chemically reactive materials
Thermally reactive materials
Piezoelectric materials
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31. DC Motors DC (direct current) motors How do they work?
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
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32. 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%
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33. 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
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34. 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)
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35. 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
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36. 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
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37. 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.
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38. How Fast do Motors Turn? Free spinning speeds (most motors):
RPM (revolutions per minute) [ 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
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39. Readings F. Martin: Section 4.1 M. Matarić: Chapters 2, 4
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