Autonomous Mobile RobotsCPE 470/670

Lecture 10

Instructor: Monica Nicolescu

CPE 470/670 - Lecture 10 2

Review

• The Subsumption Architecture

– Herbert, Genghis, The Nerd Herd, Tom and Jerry

– Advantages and dissadvantages

• Behavior-based control

– Definitions

– Principles of behavior-based control

– Toto: a behavior-based mapping robot

CPE 470/670 - Lecture 10 3

What Is a Behavior?

• Behavior-achieving modules

Rules of implementation

• Behaviors achieve or maintain particular goals (homing, wall-following)

• Behaviors are time-extended processes

• Behaviors take inputs from sensors and from other

behaviors and send outputs to actuators and other

behaviors

• Behaviors are more complex than actions (stop, turn-

right vs. follow-target, hide-from-light, find-mate etc.)

CPE 470/670 - Lecture 10 4

Principles of BBC Design

• Behaviors are executed in parallel, concurrently

– Ability to react in real-time

• Networks of behaviors can store state (history),

construct world models/representation and look into

the future

– Use representations to generate efficient behavior

• Behaviors operate on compatible time-scales

– Ability to use a uniform structure and representation

throughout the system

CPE 470/670 - Lecture 10 5

An Example Task: Mapping

• Design a robot that is capable of:

– Moving around safely

– Make a map of the environment

– Use the map to find the shortest paths to particular places

• Navigation & mapping are the most common mobile

robot tasks

CPE 470/670 - Lecture 10 6

Map Representation

• The map is distributed over different behaviors

• We connect parts of the map that are adjacent in the

environment by connecting the behaviors that

represent them

• The network of behaviors represents a network of

locations in the environment

• Topological map: Toto (Matarić ’90)

CPE 470/670 - Lecture 10 7

Toto’s Controller

CPE 470/670 - Lecture 10 8

Landmark Detection

• Keep track of what was sensed and how it was

moving

– meandering cluttered area

– constant compass direction, go straight left, right walls

– moving straight, both walls corridor

CPE 470/670 - Lecture 10 9

Building a Map

• Each landmark was stored in a behavior

• Whenever a new landmark was discovered a new behavior was added

• Adjacent landmarks are connected by communication wires

• This resulted in a topological representation of the environment, i.e., a topological world model

CPE 470/670 - Lecture 10 10

Toto’s Mapping Behaviors

• Each landmark was stored

in a behavior

• Each such landmark

behavior stored

(remembered) some

information

– landmark type (wall,

corridor, irregular)

– compass heading

– approximate length/size

– some odometry

my-behavior-type: corridor

my-compass-direction: NV

my-approximate-location: x,y

my-approximate-length: length

whenever received (input)

if input(behavior-type)= my-

behavior-type

and input (compass-direction)

= my-compass-direction

then

active <- true

CPE 470/670 - Lecture 10 11

Localization

• Whenever a landmark is detected,

its description (type, compass

direction, etc.) is sent to all

behaviors in parallel the one

that matches becomes active

• When nothing in the map matches

a new place/landmark was

discovered and added to the map

• If an existing behavior was

activated, it inhibited any other

active behaviors

CPE 470/670 - Lecture 10 12

Getting Around

• Toto can use the map to navigate

• The behavior that corresponds to

the goal sends messages (spreads activation) to all of its neighbors

• The neighbors send messages to

their neighbors in turn

• So on, until the messages reach

the currently active behavior

• This forms path(s) from the

current state to the goal

CPE 470/670 - Lecture 10 13

Path Following

• Toto did not keep a sequence of behaviors

• Instead, messages were passed continuously

– Useful in changing environments

• How does Toto decide where to go if multiple

choices are available?

– Rely on the landmark size: behaviors add up their own

length as messages are passed from one behavior to

another give path length

– Choose the shortest path

• Thus, one behavior at a time, it reached the goal

CPE 470/670 - Lecture 10 14

Toto - Video

CPE 470/670 - Lecture 10 15

Expression of Behaviors

• Example:

– Going from a classroom to another

• What does it involve?

– Getting to the destination from the current location

– Not bumping into obstacles along the way

– Making your way around students on corridors

– Deferring to elders

– Coping with change in the environment

CPE 470/670 - Lecture 10 16

Stimulus-Response Diagrams

• Behaviors are represented as a general response to

a particular stimulus

move-to-class

avoid-object

dodge-student

stay-right

defer-to-elder

COORDINATOR

class location

detected object

detected student

detected path

detected elder

Actions

CPE 470/670 - Lecture 10 17

Finite State Acceptor Diagrams

• Useful for describing

aggregations and sequences

of behaviors

• Finite state acceptor:– Q: set of allowable behavioral

states

: transition function from

(input, current state) new

state

– q0: initial state

– F: set of accepting (final) states

Start Journey Lost

At-class

move error

reached-class

other not-at-class all

all

CPE 470/670 - Lecture 10 18

Formal Methods

• Have very useful properties for the programmer

– Can be used to verify designer intentions

– Provide a framework for formal analysis of the program,

adequacy and completeness

– Facilitate the automatic generation of control systems

– Provide a common language for expressing robot

behaviors

– Provide support for high-level programming language

design

CPE 470/670 - Lecture 10 19

Situated Automata

• Kaelbling & Rosenschein (’91)

• Controllers are designed as logic circuits

• Capable of reasoning

• REX: Lisp-based language– First language to encode a reactive systems with

synchronous digital circuitry

• Gapps: goals are specified more directly– Achievement, maintenance, execution

– Logical boolean operators are used to create higher-level

goals generate circuits

CPE 470/670 - Lecture 10 20

Situated Automata

(defgoalr (ach in-classroom)

(if (not start-up)

(maint (and (maint move-to-classroom) (maint avoid-

objects)

(maint dodge-students)

(maint stay-to-right-on-path)

(maint defer-to-elders)

)

)

)

)

CPE 470/670 - Lecture 10 21

Discrete Behavioral Encoding

• The behavior consists of a set finite set of (situation, response) pairs

• Rule-based systems

IF antecedent THEN consequent

• Condition-action production rules (Nilsson ’94)– Produce durative actions: “move at 5m/s” vs. “move 5 m”

• The Subsumption Architecture

(whenever condition & consequent)

(defbehavior name & inputs outputs declarations

processes)

CPE 470/670 - Lecture 10 22

Continuous Behavioral Encoding

• Continuous response provides a robot an infinite

space of potential reactions to the world

• A mathematical function transforms the sensory

input into a behavioral reaction

• Potential fields

– Law of universal gravitation: potential force drops off with

the square of the distance between objects

– Goals are attractors and obstacles are repulsors

– Separate fields are used for each object

– Fields are combined (superposition) unique global field

CPE 470/670 - Lecture 10 23

Potential Fields

2distance

1Force Ballistic goal

attraction fieldSuperposition of two fields

CPE 470/670 - Lecture 10 24

Potential Fields

• Advantages

– Provide an infinite set of possibilities of reaction

– Highly parallelizable

• Disadvantages

– Local minima, cyclic-oscillatory behavior

– Apparently, large amount of time required to compute the

entire field: reaction is computed only at the robot’s position!

CPE 470/670 - Lecture 10 25

Motor Schemas

• Motor schemas are a type of behavior encoding

– Based on neuroscience and cognitive science

• They are based on schema theory (Arbib)

– Explains motor behavior in terms of the concurrent control

of many different activities

– Schemas store how to react and the way the reaction can

be realized: basic units of activity

– Schema theory provides a formal language for connecting

action and perception

– Activation levels are associated with schemas, and

determine their applicability for acting

CPE 470/670 - Lecture 10 26

Visually Guided Behaviors

• Michael Arbib & colleagues constructed computer models of

visually guided behaviors in frogs and toads

• Toads & frogs respond visually to

– Small moving objects feeding behavior

– Large moving objects fleeing behavior

• Behaviors implemented as a vector field (schemas)

– Attractive force (vector) along the direction of the fly

• What happens when presented with two files simultaneously?

– The frog sums up the two vectors and snaps between the two

files, missing both of them

CPE 470/670 - Lecture 10 27

Motor Schemas

• Provide large grain modularity

• Schemas act concurrently, in a cooperative but

competing manner

• Schemas are primitives from which more complex

behaviors (assemblages can be constructed)

• Represented as vector fields

CPE 470/670 - Lecture 10 28

Examples of Schemas

• Obstacle avoid and stay on corridor schemas

Rd

SdRRS

dSSd

Vmagnitude

0

2/2/

2/

WdGW

dWdP

Vmagnitude

CPE 470/670 - Lecture 10 29

Schema Representation

• Responses represented in uniform vector format

• Combination through cooperative coordination via

vector summation

• No predefined schema hierarchy

• Arbitration is not used

– each behavior has its contribution to the robot’s overall

response

– gain values control behavioral strengths

• Here is how:

CPE 470/670 - Lecture 10 30

The Role of Gains in Schemas

• Low gain • High gain

CPE 470/670 - Lecture 10 32

Foraging Example

CPE 470/670 - Lecture 10 34

Schema-Based Robots

• At Georgia Tech (Ron Arkin)• Exploration

• Hall following

• Wall following

• Impatient waiting

• Navigation

• Docking

• Escape

• Forage

CPE 470/670 - Lecture 10 35

Behavior Coordination

• Behavior-based systems require consistent

coordination between the component behaviors for

conflict resolution

• Coordination of behaviors can be:

– Competitive: one behavior’s output is selected from

multiple candidates

– Cooperative: blend the output of multiple behaviors

– Combination of the above two

CPE 470/670 - Lecture 10 36

Competitive Coordination

• Arbitration: winner-take-all strategy only one response chosen

• Behavioral prioritization

– Subsumption Architecture

• Action selection/activation spreading (Pattie Maes)

– Behaviors actively compete with each other

– Each behavior has an activation level driven by the robot’s

goals and sensory information

• Voting strategies (DAMN architecture, Rosenblatt)

– Behaviors cast votes on potential responses

CPE 470/670 - Lecture 10 38

Cooperative Coordination

• Fusion: concurrently use the output of multiple behaviors

• Major difficulty in finding a uniform command

representation amenable to fusion

• Fuzzy methods

• Formal methods

– Potential fields

– Motor schemas

– Dynamical systems

CPE 470/670 - Lecture 10 39

Emergent Behavior

The resulting robot behavior may sometimes be

surprising or unexpected

emergent behavior

CPE 470/670 - Lecture 10 40

Wall Following

• A simple wall following controller:

– If too close on left-back, turn left

– If too close on left-front, turn right

– Similarly for right

– Otherwise, keep straight

• If the robot is placed close to a wall it will follow

• Is this emergent?

– The robot has no explicit representations of walls

– The controller does not specify anything explicit about

following

CPE 470/670 - Lecture 10 41

Emergence

• A “holistic” property, where the behavior of the robot

is greater than the sum of its parts

• A property of a collection of interacting components

– A robot’s interaction with the environment

– The interaction of behaviors

• Often occurs in reactive and behavior-based

systems (BBS)

• Typically exploited in reactive and BBS design

CPE 470/670 - Lecture 10 42

Flocking

• How would you design a flocking behavior for a

group of robots?

• Each robot can be programmed with the same

behaviors:

– Don’t get too close to other robots

– Don’t get too far from other robots

– Keep moving if you can

• When run in parallel these rules will result in the

group of robots flocking

CPE 470/670 - Lecture 10 43

Emergent Behavior

• Emergent behavior is structured behavior that

is apparent at one level of the system (the

observer’s point of view) and not apparent at another (the controller’s point of view)

• The robot generates interesting and useful behavior

without explicitly being programmed to do so!!

• E.g.: Wall following can emerge from the interaction

of the avoidance rules and the structure of the

environment

CPE 470/670 - Lecture 10 44

Components of Emergence

• The notion of emergence depends on two

components

– The existence of an external observer, to observe the

emergent behavior and describe it

– Access to the internals of the controller, to verify that the

behavior is not explicitly specified in the system

• The combination of the two is, by many researchers,

the definition of emergent behavior

CPE 470/670 - Lecture 10 45

Unexpected & Emergent Behavior

• Some argue that the description above is not

emergent behavior and that it is only a particular

style of robot programming

– Use of the environment and side-effects leads to the novel

behavior

• Their view is that emergent behavior must be

truly unexpected, and must come to a surprise to

the external observer

CPE 470/670 - Lecture 10 46

Expectation and Emergence

• The problem with unexpected surprise as property

of behavior is that:

– it entirely depends on the expectations of the

observer which are completely subjective

– it depends on the observer’s knowledge of the

system (informed vs. naïve observer)

– once observed, the behavior is no longer

unexpected

CPE 470/670 - Lecture 10 47

Emergent Behavior and Execution

• Emergent behavior cannot always be designed in advance and

is indeed unexpected

• This happens as the system runs, and only at run-time can

emergent behavior manifest itself

• The exact behavior of the system cannot be predicted

– Would have to consider all possible sequences and combinations

of actions in all possible environments

– The real world is filled with uncertainty and dynamic properties

– Perception is affected by noise

• If we could sense the world perfectly, accurate predictions could

be made and emergence would not exist!

CPE 470/670 - Lecture 10 48

Desirable/Undesirable Emergent Behavior

• New, unexpected behaviors will always occur in any

complex systems interacting with the real world

• Not all behaviors (patterns, or structures) that

emerge from the system's dynamics are desirable!

• Example: a robot with simple obstacle avoidance

rules can oscillate and get stuck in a corner

• This is also emergent behavior, but regarded as a

bug rather than a feature

CPE 470/670 - Lecture 10 49

Sequential and Parallel Execution

• Emergent behavior can arise from interactions of the

robot and the environment over time and/or over space

• Time-extended execution of behaviors and interaction with the environment (wall following)

• Parallel execution of multiple behaviors (flocking)

• Given the necessary structure in the environment and

enough space and time, numerous emergent

behaviors can arise

CPE 470/670 - Lecture 10 50

Architectures and Emergence

• Different architectures have different methods for dealing with emergent behaviors: modularity directly affects emergence

• Reactive systems and behavior-based systems exploit emergent behavior by design– Use parallel rules and behaviors which interact with each

other and the environment

• Deliberative systems and hybrid systems aim to minimize emergence– Sequential, no interactions between components, attempt

to produce a uniform output of the system

CPE 470/670 - Lecture 10 51

Readings

• M. Matarić: Chapters 17, 18