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Advanced Mobile Robotic The City College of the City University of New York Introduction to AI Robotics. Instructor: Prof. Jizhong Xiao Presenters: Diana Acevedo Sefton Bennett Clara Nieto-Wire Jorge O. Peche Marios Timotheou March 13, 2006.

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slide1
Advanced Mobile RoboticThe City College of the City University of New YorkIntroduction to AI Robotics

Instructor: Prof. Jizhong Xiao

Presenters: Diana Acevedo

Sefton Bennett

Clara Nieto-Wire

Jorge O. Peche

Marios Timotheou

March 13, 2006

reactive paradigm
Reactive Paradigm
  • Biological foundations of the reaction paradigm
  • The reactive paradigm
  • Designing a reactive implementation
slide4
Why??????
  • Animals  Open World
  • AGENT  Self-Contained and Independent

Interacts with the world to make changes or sense what is happening.

Humans-Animals-Robots = AGENT

marr s computational theory
Marr’s Computational Theory

Level 1

What are we trying to represent?

Level 2

How Represent it?

Level 3

How is it implemented?

computational theory levels
Computational Theory Levels
  • Existent proof of what can/should be done. Agents share commonality of purpose or functionality.
  • “What”=Inputs, outputs and transformations. Creating Flow Charts of black boxes. Agents exhibit common processes.
  • How to implement the processes. Agents may have little or not commonality in their implementations.
behavior

PATTERN OF

MOTOR ACTIONS

SENSORY

INPUT

BEHAVIOR

BEHAVIOR

Mapping of sensory inputs to a pattern of motor actions which then are used to achieve a task.

Pattern  Actions and sequences that are

always the same.

types of behavior
Types of Behavior
  • Reflexive : Stimulus-Response.
  • Reactive : Learn-Consolidate
  • Conscious : Deliberative

Reactive Paradigm  Reflexive Behavior

reflexive behavior
Reflexive Behavior

NO CONGNITION

  • Reflexes : TRESPONSE = TSTIMULUS
  • Taxes : Response  Movement
  • Fixed-Action Patterns:

TRESPONSE > TSTIMULUS

ways to acquire behaviors
…Ways to Acquire Behaviors
  • Innate
  • Sequence of innate behaviors
  • Innate with memory
  • Learn
innate releaser mechanisms

IRM  Activates

the Behavior

PATTERN OF

MOTOR ACTIONS

SENSORY

INPUT

BEHAVIOR

Innate Releaser Mechanisms
  • Releaser: Control Signal

(internal or external).

  • Behaviors can be implicitly chained together by their releasers.
concurrent behaviors
Concurrent Behaviors
  • Equilibrium 

Behaviors balance each other.

  • Dominance of one  Winner takes all.
  • Cancellation 

Behaviors cancel each other.

perception

WORLD

Acts &

Modifies

World

Samples, Finds

Potential Actions

Directs what

to look for

COGNITIVE

ACTIVITY

PERCEPTION

OF

ENVIROMENT

PERCEPTION
  • Cognitive activities (Feedback and Feed forward control)
    • Planning
    • Reacting
  • Two uses of perception
    • Release a behavior
    • Guide a behavior
gibson s ecological approach
Gibson’s Ecological Approach
  • Affordances : Perceivable potentialities of the environment for an action.
    • Directly Perceivable.
    • Sensing does not need/require memory or interpretation (optic flow).
  • Structural models attempt to describe an object in terms of physical components.
slide15

Neisser: Two perceptual systems

  • Direct Perception: Gibsonian or ecological track of the brain. Structures low in the brain that evolved earlier. (Affordances)
  • Recognition: More recent perceptual track in the brain.
schema theory
Schema Theory

Schema  Knowledge of how to act and/or perceive.

Computational process to accomplish the activity.

  • schema instantation is specific to a situation, equivalent to an instance in OOP.
slide17

Motor Schema

Behavior = Shema

RELEASERS

Perceptual Schema

objectives
Objectives
  • Define what the reactive paradigm is in terms of :
    • i) the three primitives SENSE, PLAN, and ACT
    • ii) sensing organization.
  • List and discuss the characteristics and connotations of a reactive robotic system.
  • Describe the two dominant methods for combining behaviors in a reactive architecture.
    • i) subsumption
    • ii) potential field summation.
  • Evaluate these two reactive architectures in terms of:
    • i) support of modularity
    • ii) niche targetability
    • iii) ease of portability to other domains
    • iv) robustness
  • Be able to program a behavior using a p.field methodology.
  • Be able to construct a new p.field from primitive p.fields, and sum the p.fields to generate and emergent behavior.
slide20

HierarchicalParadigm

is characterized by having a horizontal decomposition.

Reactive Paradigm

is characterized by having a vertical decomposition.

  • If anything happens to an advanced behavior, the lower behaviors would still operate.
  • All reactive systems are composed of behaviors.
attributes of reactive paradigm
Attributes of Reactive Paradigm
  • Behaviors.
  • SENSE-ACT organization.
  • Behavior specific (local) sensing.
slide22

Attributes of Reactive Paradigm

BEHAVIOR

(Ethological def.) a direct mapping of sensory inputs to a pattern of motor actions that are then used to achieve a task.

(Mathematically) a transfer function , transforming sensory inputs into actuator commands.

Schema: a way of expressing the basic unit of activity. It is consists of the knowledge of how to act or perceive, and the algorithm by which it uses to accomplish the activity.

(like a generic template for doing some activity).

sense act organization s a

Attributes of Reactive Paradigm

SENSE-ACT Organization (S-A)

The SENSE and ACT components are tightly coupled into behaviors and all robotic activities emerge as the result of these behaviors operating either in sequence or concurrently. (NO PLAN component).

The S-A organization does not specify how the behaviors are coordinated.

What happens when multiple behaviors are active simultaneously?

slide24

Attributes of Reactive Paradigm

Behavior-Specific (local) Sensing

  • Sensing is local
  • Sensors can be shared
  • Sensors can be fused locally by a behavior
  • Behaviors favor the use of affordances (direct perceptions). The behavior does not rely on any central representation built up from all sensors. The sensing portion of the behavior is nearly instantaneous and action is very rapid.
connotations of reactive behaviors
Connotations of Reactive Behaviors:
  • A reactive robotic system executes rapidly.

Behaviors can be implemented directly in hardware as circuits or with low computational complexity.

  • Reactive robotic systems have no memory

Reactive behaviors are limited to pure “stimulus-response reflexes”

BEHAVIORS ARE CONTROLLED BY WHAT IS HAPPENING IN THE WORLD, DUPLICATING THE SPIRIT OF INNATE RELEASING MECHANISMS, rather than by the program storing and remembering what the robot did last.

characteristics of reactive behaviors
Characteristics of Reactive Behaviors:
  • Robots are situated agents (integral part of the world) operating in an ecological niche (goals of robot, world where it operates, and how it perceives the world).
  • Behaviors serve as the building blocks for robotic actions. And the overall behavior of the robot is emergent.
  • Only local, behavior-specific sensing is permitted.

(Any sensing which does require representation is expressed in ego-centric coordinates).

  • These systems inherently follow good software design principles.
  • Animals models of behavior are often cited as a basis for these systems or a particular behavior.
advantages of programming by behavior
Advantages of programming by behavior:

Behaviors support good software engineering principles through:

  • decomposition
  • modularity and
  • incremental testing.
representative architectures
Representative Architectures
  • Potential Field Methodologies
  • Subsumption Architecture

Provide mechanisms for:

  • Determine what happens when multiple behaviors are active at the same time
  • Triggering behaviors
subsumption architecture
Subsumption Architecture
  • Subsumption refers to how behaviors are combined.
  • In many applications behaviors are embedded directly in the hardware or on small microprocessors.
slide30

Subsumption Philosophy

  • Modules should be grouped into layers of competence
  • Modules in a higher lever can override or subsume behaviors in the next lower level
    • Suppression: substitute input going to a module
    • Inhibit: turn off output from a module
  • No internal state in the sense of a local, persistent representation similar to a world model.
  • Architecture should be taskable: accomplished by a higher level turning on/off lower layers
subsumption architecture1

EXAMPLE

  • Level 0 Avoid collisions
Subsumption Architecture

Vector representation of sonar readings

Level 0 recast as primitive

behaviors

  • Robot-centric polar plot of 8 sonar range readings

Polar plot unrolled into a plot

slide32

Subsumption Architecture

  • EXAMPLE LEVEL 1: WANDER
  • Level 1: Wander
  • Level 0 : Avoid Collisions
  • Inhibition
  • Suppression

Level 1 recast as primitive behaviors

subsumption architecture2

EXAMPLE LEVEL 2: Follow Corridors

Level 2: Follow Corridors

Level 1: Wander

Level 0 : Avoid Collisions

Subsumption Architecture
subsumption summary
Subsumption Summary
  • Subsumption groups schema-like modules into layers of competence, or abstract behaviors.
  • Higher layers may subsume and inhibit behaviors in lower layers, but behaviors in lower layers are never rewritten or replaced.
  • The design of layers and component behaviors is difficult. It is more of an art than a science. (True for all reactive architectures)
  • Behaviors are released by the presence of stimulus in the environment. (No PLAN component)
  • The releaser is almost always the percept for guiding the motor schema.
  • Perception is ego-centric and distributed. (sonar plot was relative to the robot, available to any behavior which needed it, and the output from perceptual schemas can be shared with other layers)
potential fields ron arkin
Potential Fields:Ron Arkin

From http://www.cc.gatech.edu/aimosaic/faculty/arkin

From http://www.cc.gatech.edu/aimosaic/robot-lab/MRLhome.html

potential fields philosophy
Potential Fields Philosophy
  • The motor schema (what the robot should do) component of a behavior can be expressed with a potential field’s methodology
    • A potential field can be a “primitive” or constructed from primitives which are summed together
    • The output of behaviors are combined using vector summation
  • From each behavior, the robot “feels” a vector or force
    • Magnitude = force, strength of stimulus, or velocity
    • Direction
  • We visualize the “force” as a field, where every point in space represents the vector that it would feel if it were at that point
slide37

Potential fields are continuous

  • Even with a small element at a point can be associated with a vector
  • How an obstacle will exert a field on the robot and make it run away?
  • If the robot is close to the obstacle it is inside the potential field and will feel a force that makes it want to face directly away from the obstacle and move away.
  • If the robot is not within that range then it just sits there because there is no force on it.
5 primitive potential fields
5 Primitive Potential Fields

a. Uniform b. Perpendicular c. Attraction d. Repulsion e. Tangential

common fields in behaviors
Common fields in behaviors
  • Uniform
    • Move in a particular direction, corridor following
  • Repulsion
    • Runaway (obstacle avoidance)
  • Attraction
    • Move to goal
  • Perpendicular
    • Corridor following
  • Tangential
    • Move through door, docking (in combination with other fields)
  • random
    • do you think this is a potential field?
slide41

Magnitude Profiles

  • The length of the arrows gets smaller closer to the object and the way the magnitude of the vectors in the field change is called the magnitude profile.
  • Magnitude profiles solve the problem of constant magnitude, which allows the designer to represent reflexivity (response is proportional to the strength of the stimulus).
  • The main motivation for magnitude profiles is to fine-tune the behavior.
slide42

Linear Drop off

If a robot is far away from the object it will turn and move quickly towards it, then slows up to keep from hitting the object.

Mathematically, the rate at which the magnitude of the vectors drops off can be plotted as a straight line.

Exponential Drop off

To have the robot react more the closer it is to the object (constant magnitude). Stronger reaction but more of a taper.

The drop off is proportional to the square of the distance for every unit of distance away from the object, the force on the robot drops in half.

slide43

Potential Fields and Perceptions

  • Potential Fields are ego-centric because robots perception is ego-centric.
  • This makes programming easy. The visualization of the entire field may appear to indicate that the robot and the objects are in a fixed, absolute coordinate system but they are not.

The robot computes the effect of the potential field, usually as a straight line, at every update, with no memory of where it was previously or where the robot has moved.

combining fields for emergent behavior

goal

goal

obstacle

obstacle

Combining Fields for Emergent Behavior

obstacle

If robot were dropped anywhere on this grid,

it would want to move to goal and avoid obstacle:

Behavior 1: MOVE2GOAL(attraction field)

Behavior 2: RUNAWAY (repulsive field)

The output of each independent behavior is a vector,

the 2 vectors are summed to produce emergent behavior

fields and their combination

Note: in this example, robot can sense the

goal from 10 meters away

Note: In this example, repulsive field only extends for 2 meters;

the robot runs away only if obstacle within 2 meters

Fields and Their Combination
path taken
Path Taken

Robot only feels

vectors for this

point when it (if)

reaches that point

  • If robot started at this location, it would take the following path
  • It would only “feel” the vector for the location, then move accordingly, “feel” the next vector, move, etc.
  • Pfield visualization allows us to see the vectors at all points, but robot never computes the “field of vectors” just the local vector
slide47

Update rates, Holonomicity and Local Minima

1. Distance between updates are different (length of the arrows)

Note between t3 and t4, the robot actually goes further without turning and has to turn back as to go to goal. A smoother path would have a faster update.

2. Robots turn in any direction but have to stop and errors develop due to the contact between the wheels and the surface.

3. The vectors only have head and no body, this means the magnitude is 0.0 and that if the robot reaches to that spot it will stop and not move.

how does the robot see a wall without reasoning or intermediate representations
How does the robot see a wall without reasoning or intermediate representations?
  • Perceptual schema “connects the dots”, returns relative orientation

MS: Perp.

orientation

PS:

Find-wall

S

MS: Uniform

Sonars

Sum all 8 ranges internally

Return the single largest distance and direction

ok but why isn t that a representation of a wall
OK, But why isn’t that a representation of a wall?
  • It’s not really reasoning that it’s a wall, rather it is reacting to the stimulus which happens to be smoothed (common in neighboring neurons)
level 0 runaway

Note: multiple instances of

a behavior vs. 1:

Could just have 1

Instance of RUN AWAY,

Which picks nearest reading;

Doesn’t matter, but this

Allows addition of another

Sonar without changing the

RUN AWAY behavior

Level 0: Runaway
level 1 wander
Level 1: Wander

Wander is

Uniform, but

Changes direction

aperiodically

example follow corridor or follow sidewalk
Example: follow-corridor or follow-sidewalk

Uniform

Perpendicular

Note use of

Magnitude profiles:

Perpendicular decreases

Combined

pfields
Pfields
  • Advantages
    • Easy to visualize
    • Easy to build up software libraries
    • Fields can be parameterized
    • Combination mechanism is fixed, tweaked with gains
  • Disadvantages
    • Local minima problem (sum to magnitude=0)
    • Jerky motion
example docking behavior

Orientation, ratio of pixel countstangent vector

Total countattraction vector

  • Arkin and Murphy, 1990, Questa, Grossmann, Sandini, 1995, Tse and Luo, 1998, Vandorpe, Xu, Van Brussel, 1995. Roth, Schilling, 1998, Santos-Victor, Sandini, 1997
Example: Docking Behavior
pfields summary
Pfields Summary
  • Reactive Paradigm: SA, sensing is local
    • Solves the Open World problem by emulating biology
    • Eliminates the frame problem by not using any global or persistent representation
    • Perception is direct, ego-centric, and distributed
  • Two architectural styles are: subsumption and pfields
  • Behaviors in pfield methodologies are a tight coupling of sensing to acting; modules are mapped to schemas conceptually
  • Potential fields and subsumption are logically equivalent but different implementations
  • Pfield problems include
    • local minima (ways around this)
    • jerky motion
    • bit of an art
objective
Objective
  • Use schema theory to design and program behavior using object-oriented programming principle.
  • Design a complete behavioral system, including coordinating and controlling multiple concurrent behaviors.
  • Describe the two methods for assembling primitive behaviors into abstract behaviors: finite state machines and scripts.
slide62

Emergent Behavior

  • The reactive movement is characterized by robotics running a very small set of behavior which are combined internally to produce an overall emergent behavior.
  • The key components of a reactive architecture are the behavior plus the mechanism for merging the output of the concurrent behaviors.
  • Series of Behaviors are operating in recognizable sequence such as Pick Up the Trash Robot
behavior as object in oop
Behavior as Object in OOP
  • Schema is well suited for transferring theoretical concept in Object-Oriented Programming (OOP).
  • Schema theory is used as a bridge between concept in biological intelligence and robotic (reactivity and affordance).
  • A schema as a programming object would be a class: Coordinate Control Program, Behavior, Motor Schema, and Perceptual Schema.
behavior as object in oop1
Behavior as Object in OOP
  • Perceptual Schema takes sensor input and transforms it into data structure called a percept; it is linked to the sensor.
  • Motor Schema transform the percept into a vector or other form of representing an action; it is linked to the robot’s actuators
  • Behaviors are composed of at least one Perceptual Schema and one Motor Schema, these schemas act as the methods for Behavior classes.
primitive and abstract behaviors
Primitive and Abstract Behaviors
  • Primitive behavior is composed of only one perceptual schema and one motor schema, and there is no need to have any coordinated control program.
  • Monolithic
  • Simple mapping
  • Simple method (no multiple methods or objects.
  • Abstract behavior is composed of multiples perceptual schema and motor schema, and there are farther removed from the sensor and actuator than the primitive behavior.
unmanned robotics competition
Unmanned Robotics Competition
  • Describe the task: specify what the robot has to do to be successful.
  • Describe the robot: determine the basic physical abilities of the robot and any limitation.
  • Describe the environment: determine the situatedness of the robot and identifies perceptual opportunity for the behavior (Perceptual Schema and its functionality).
  • Describe how the robot should act in response to its environment:Identify the set of one or more candidate primitive behavior
unmanned robotics competition1
Unmanned Robotics Competition
  • Refine each behavior: concentrate on the design of each individual behavior (reactive organization system and activities).
  • Test each behavior independently.
  • Test with other behavior: Perform integration testing, where the behaviors are combined. This includes testing in actual environment.
finite state automata fsa
Finite State Automata (FSA)
  • Finite State Automata (FSA)
  • A set of popular mechanism for specifying what a program should be doing at a given time.
  • Can be represented as a state diagram or a table
  • M = { K, Σ, δ, s, F }
  • M : finite state machine
  • K : set of all states q ε K
  • Σ : set of all inputs σεΣ
  • δ : transitions between states
  • s : starting state
  • F: terminated ( final ) state
finite state automata fsa2
Finite State automata (FSA)

Relationship between FSA and behaviors

finite state automata fsa3
Finite State automata (FSA)

Pick Up the Trash FSA

finite state automata fsa4
Finite State automata (FSA)

Pick Up the Trash FSA

finite state automata fsa5
Finite State automata (FSA)
  • Pick Up the Trash FSA
finite state automata fsa6
Finite State automata (FSA)
  • Advantages
    • Formal mechanism
    • Have to fill in the table, so a way of spotting unforeseen effects
    • Can be implemented in a number of ways
  • Disadvantages
    • Hard to keep up with implicit behaviors
      • Ex. Avoid obstacle is often running throughout ALL states, gets tedious to express it formally
    • Tend to add explicit variable for transitions, rather than rely on emergent behavior from environment
abstract behaviors
Abstract Behaviors

Template abstract behaviors

  • pick-up-trash (trash-color, trash can color, size-trash can)
  • Different initial conditions

Imprinting

  • Detect best value for sensing. ( red can )

Exceptions

  • Substitute behavior under certain contentions (different values of red, raster scan)
scripts
Scripts
  • Originally used in natural language processing.
  • Think of the robot and the tasks in terms of a screenplay.
  • Causal chain.
scripts1
Scripts

\\ index into the correct step in the causal chain

if(EMPTY) {

if(SEE_RED) {

move_to_goal(red);

} else {

wander();

}

} else {

grab_trash();

if(NO_BLUE){

wander();

} else if (AT_BLUE){

drop_trash();

} else if (SEE_BLUE) {

move_to_goal(blue);

}

}

Pick up the trash example:

for each update…

\\ look for props and cues first: cans, trash cans, gripper

rstatus=extract_color(red, rcx, rSize);

if(rStatus==TRUE)

SEE_RED=TRUE;

else

SEE_RED=FALSE;

bStatus=extract_color(blue,bcx,bSize);

if(bStatus==TRUE){

SEE_BLUE=TRUE; NO_BLUE=FALSE;

} else {

SEE_BLUE=FALSe; NO_BLUE=TRUE;

}

AT_BLUE=looming(size,bSize);

gStatus=gripper_status();

if(gStatus==TRUE) {

FULL=TRUE; EMPTY=FALSE;

} else {

FULL=FALSE; EMPTY=TRUE;

}

scripts2
Scripts
  • Advantages
    • A more storyboard like way of thinking about the behaviors
    • If-then, switch style of programming like FSA
    • Since a Script is “in” a behavior, other behaviors such as avoid can be running concurrently without having to appear “in” the script
    • Exception handling is a big plus in Real Life
  • Disadvantages
    • Can be a bit of overkill for simple sequences, especially with C++