Transcript Hybrid Deliberative/Reactive Paradigm

The Hybrid Deliberative/Reactive
Paradigm
The City College of New York
Department of Electrical Engineering
Group Member: Jik Cheung
Yongwen Zhu
Yayi Hu
Xuezhou Ma
Junjun Li
Chapter Objectives
Describe the hybrid paradigm in terms of SAP and sensing
organization.
Distinguish the responsibilities between the deliberative layer
and reactive layer.
List the basic components of a Hybrid architecture: sequencer
agent, resource manager, cartographer, mission planner,
performance monitoring and problem solving agent.
Identify the difference between managerial, state hierarchy and
model-oriented styles of Hybrid architectures.
Be able to describe the use of state to define behaviors and
deliberative responsibilities in state hierarchy styles of Hybrid
architectures.
Overview
I. Reactive Paradigm is the major trend
by the end of the 1980’s.
• However, the robot could not…
Remember the state of the robot/world
Plan optimal trajectories
Make maps
Monitor its own performance
Select the best behaviors for a task
• Reactivity more art then science?
Should planning be reintroduced?
II.
Deliberative Vs. Planning
• Not all of these activities involve
Planning:
Make maps
Monitor its own performance
Select the best behaviors for a task
• To differentiate this from path planning,
the term deliberative was coined.
III. Hybrids
• How can slow planning be intergraded
with fast reactivity?
Five examples of architectures will be illustrated:
AuRA, SFX, 3T, Saphira and TCA.
• First Opinion: The worst of both worlds!
Reactive systems for unstructured worlds
Hierarchical systems for knowledge-rich worlds
• Nowadays: The best of both worlds!
Reactive functions for low level control
Deliberation for higher level tasks
Hybrid Paradigm
Organization: Plan, Sense-Act:
Motivation of Hybrids
• Cohesion (object oriented programming)
Reactivity:
Short time horizon (Present)
No global knowledge
Work with sensors and actuators
Deliberation:
Long time horizon (Pass, Future)
Global knowledge
Work with symbols
• Multi-tasking
Deliberative functions execute in parallel with
reactive functions.
Sensing Organization
World Map/
Eavesdrop
The Map (World Model)
Knowledge Rep
Virtual sensor
Feedback
Can have its own
sensors
Can “eavesdrop” on
other sensors
Can act as “virtual”
sensor
Behavior
Behavior
Behavior control only
Behavior
Planning only
Sensor 3
Sensor 1
Sensor 2
Skill Vs. Behaviors
• Not purely reflexive:
Reflexive (response to stimulus)
Innate (virtual sensor turns behavior on or off)
“If power is low, charge”
Learned
Retain feedback to determine best behavior sequence to
instantiate next time
• More complex emergent behaviors:
Behavior sequences
Connotations of Global
• “Global” isn’t always truly global in Hybrids.
• Behavioral Management
Planning which behaviors to use requires knowledge about
current and future world state
• Performance monitoring
Detecting task progress and sensor confliction require
knowledge about the robot hardware and the overall goals.
Nonetheless
Common Components
• Sequencer
Generates a sequence of behaviors
• Resource Manager
Allocates resources to behaviors
• Cartographer
Creates, stores, maintains, accesses map information
• Mission Planner
Interact with human and create a plan to achieve a goal
• Performance Monitor/problem solver
Determines whether the robot is making progress
toward its goal
Architecture Styles
• Managerial (division of responsibility as in
business)
AuRA
SFX
• State Hierarchies (strictly by time scope)
3T
• Model-Oriented (Model serve as virtual
sensors)
Saphira
TCA
Styles of hybrid architectures
● Managerial styles
● State hierarchies styles
● Model-oriented styles
• Managerial Architectures
Description -- top agents – high level planning
↓
subordinate agents – refine plan, gather resources
↓
lowest level agents
▲ AuRA Architectures
▲ SFX Architectures
▲ Autonomous Robot Architecture (AuRA)
It consists of five subsystems
-- planner : responsible for mission and task planning
-- cartographer : all map making, reading functions
-- motor : motor schema
-- sensor
-- homeostatic control :
modify the relationship between behaviors by changing the
gain as a function of robot or other constraints
AuRA Architectural Layout
The table below summarizes AuRA in
term of the common components and
style of emergent behavior
AuRA Summary
Sequencer Agent
Navigator, Pilot
Resource Manager
Motor Schema Manager
Cartographer
Cartographer
Mission Planner
Mission Planner
Performance Monitoring Agent
Pilot, Navigator, Mission Planner
Vector summation, spreading activation of
behaviors, homeostatic control
Emergent Behavior
▲
Sensor Fusion Effects (SFX)
description – It is an extension to AuRA. The extension
was to add modules to specify how sensing and
handling sensor failure.
Deliberative layers
-- Mission
planner : acts as a CEO giving a directions
-- effector
-- Task
-- Sensor
All of three of above determine the best allocation of
effect, sensing resource and perceptual schema.
-- Cartographer
: map making, path planning
SFX (Sensor Fusion Effects)
Recognition Cartographer Deliberative
(model/map
perception
Layer Managers
making)
Cerebral
Cortex-like
functions
Choice of behaviors, resource
allocation, motivation, context
Sensor
Behavioral
Whiteboard
Whiteboard
Parameters to behaviors,
sensor failures, task progress
Behaviors
Sense
Sense
Receptive
Sense
actions
Field
Sense
Sense
Sense
Sense
Sensor
Superior
Colliculus-like
functions
(using direct
perception, fusion)
Focus of attention,
recalibration
Muscle
Muscle
Muscle
Actuators
Reactive layers
All these layers reflect to ------- strategic behaviors and
tactical behaviors
Tactical behavior serves as filter on strategic commands to ensure
to robot acts in a safe manner in as close accordance with the
strategic intent as possible
the interaction of strategic and tactical behaviors is still considered
emergent behavior
Tactical Behaviors
sensors
inclinometer
camera
strategic behaviors
tactical behaviors
actuators
slope
safe velocity
follow-path
strategic
velocity
clutter
drive
motor
speed-control
direction to path
safe direction
steer
motor
avoid
sonar
obstacles
how much vehicle turns
swivel camera
center-camera
camera
pan
motor
The table below summarizes SFX in term of the
common components and style of emergent behavior
SFX Summary
Sequencer Agent
Task Manager
Resource Manager
Sensing and Task Manager
Cartographer
Cartographer
Mission Planner
Mission Planner
Performance Monitoring Agent
Performance Monitor, Habitat Monitor
Emergent Behavior
Strategic behaviors grouped into abstract
behaviors or scripts, then filtered by
tactical behaviors
• State-hierarchy Architectures
(3 layers)
▲ 3 – tiered (3T)
Used for : planetary rovers
underwater vehicles
robot assistants for astronauts
Structure
-- planner :
setting goal and strategic plans
-- sequencer :
select a set of primetive behaviors
develop a task network
-- skill manager :
in this layer the skills have associated events to verify explicitly
that an action has had to correct effect
3T Architecture
The table below summarizes 3T in term of the common
components and style of emergent behavior
3T
Sequencer Agent
Sequencer
Resource Manager
Sequencer (Agenda)
Cartographer
Planner
Mission Planner
Planner
Performance Monitoring Agent
Planner
Emergent Behavior
Behaviors grouped into skills,
skills grouped into task network
• Model-oriented Architectures
two of best-known model-oriented architecture
▲Saphira architecture
▲Task Control Architecture
▲Saphira Architecture
-- PRS-Lite
it is capable of taking natural language voice commands from
humans and then operationalizing that into navigation tasks and
perceptual recognition routines.
-- virtual sensor
-- navigation tasks
manage the behaviors
-- LPS (Local Perceptual Space)
determine the planning and execution
improve the quality of the robot’s overall behavior
Saphira Architecture
The table below summarizes Saphira in term of the
common components and style of emergent behavior
Saphira
Sequencer Agent
Topological planner, Navigation Tasks
Resource Manager
PRS-Lite
Cartographer
LPS
Mission Planner
PRS-Lite
Performance Monitoring Agent
PRS-Lite
Emergent Behavior
Behaviors fused with fuzzy logic
▲Task Control Architecture (TCA)
-- Task
Scheduling (Mission Planner)
determine the goal and order of execution
-- Path Planning (Cartographer)
-- Navigation (Sequencer)
to determine what the robot should be looking for, where it is,
where it has been.
-- Obstacle Avoidance
To factor in not only obstacle but how to respond with a smooth
trajectory for the robot’s current velocity.
TCA
The table below summarizes TCA in term of the common
components and style of emergent behavior
TCA
Sequencer Agent
Navigation Layer
Resource Manager
Navigation Layer
Cartographer
Path-Planning Layer
Mission Planner
Task Scheduling Layer
Performance Monitoring
Agent
Navigation, Path-Planning, TaskScheduling
Emergent Behavior
Filtering
Basic Important concept
• Paradigm
Paradigm is both a way of looking at the world and
an implied set of tools for solving problems.
• Sense, Plan, Act.
Commonly accepted robotic primitives.
Robotics have to go through these three, or at least
two process to complete a mission.
• Local Processing and Global World Model
Local: sensor data used in specific for each function.
Global: all sensor data is processed to single model.
Hierarchical Paradigm
• What are the two main features?
Robot operates in a top-down fashion.
All sensor data tends to be gathered to one global
world model. A single representation that planner
can use to rout the action.
SENSE
PLAN
ACT
Reactive Paradigm
• What are the two main features?
Throw out planning all together.
The inputs to an act are the direct output of a sensors.
examine living example of intelligence.
SENSE
ACT
Hybrid Paradigm
• Features of Hybrid Deliberative/Reactive
Paradigm
It is reactive planning, Planning to subtask is
done at one step.
Deliberative planning take a long time comparing
to the time of reactive execution
Sensor data go directly to each behavior but is
also available to the planner for construction of
task-oriented global world model.
Model-based Architecture focuses on the creation
and maintenance of a global world model.
Hybrid Paradigm
• The basic models of Hybrid Paradigm
Sequencer: generate a set of behaviors for subtasks.
Resource manger: allocate resources to behavior
Cartographer: for creating, storing, maintaining
map or spatial information.
Mission Planner: interact with man, construct a
mission plan.
Performance Monitoring: monitor the process of the
executing, It’s self-awareness.
Hybrid Paradigm
Plan
SENSE
ACT
Hybrid Paradigm
Robot Primitive
PLAN
BEHAVIOR
Input
Information(
sensed and
cognitive )
Sensed data
output
Directives
Actuator
command
Other Hybrid Paradigm
• DARPA UGV Demo II and Demo III.
Outdoor ground vehicle control and navigation.
given a map and a set of directions find enemy
location.
Reach in automating highway vehicles by
European Community ESPRIT agency and some
United States agency
Autonomous planetary rovers by NASA.
Mapping planetary surface, planning path.
Advantages of Hybrid
Architecture is highly modular
Architecture is highly modular of the
deliberative with object-oriented programming.
•
Full knowledge of environment
Software agents can use agent-specific
abstractions to exploit the structure of an
environment in order to fulfill their particular
role in deliberation.
•
Use of Global models
Global models are only for symbolic functions
and Planners( sequencers) often produce partial
plans.
Advantages of Hybrid
• Execution is reactive.
• No frame problems.
In the Hybrid Paradigm almost no the frame
problems resulted by the Hierarchical.
• Self-consciousness.
Ensure robustness by monitoring the performance
of the robot and self-diagnosing, this is called
self-consciousness.
Examples For Good of
The Reactive
• Example1
we don’t need to turn all sensed data to global model
to use in order to accuracy, convince, reliability, and
saving time.
• Example 2
in Hierarchical Paradigm it is unwise in a lot of
practical problems to block out the sensed data to
Behaviors( Actuator).
A/D
D/A
Sensor 1
CPU
Sensor 2
II.
LED
Sensor 3
Pressure Sensor
f
A/D
Gas
Sensor
Alarm
1
A/D
D/A
CPU
A/D
D/A
Interleaving Deliberation
and Reactive Control
• For navigation
Deliberation: Cartographer( planner) generates a
complete optimal route, decompose the route to
segments-waypoints.
Reactive Control: Waypoint can be accomplished by
behaviors.
• Top-down method
Deliberative layers decompose the missions to finer
steps. Reactive layers accomplish the first sub-goal.
Interleaving Deliberation
and Reactive Control
• Bottom-up method.
Deliberative layers act as virtual sensors. The
analyzed information as a sensed data input into
behaviors( reactive layers)-Bottom-up
• Other functions of Deliberations
In the deliberative layers, sequencer must know
why a failure and know the need to change the
behaviors and alert the human supervisor.-selfconsciousness.
Summary of AI Robotics
Ch.1: From Teleoperation to
Autonomy
• What is intelligent robots?
• What is the difference between AI and
Engineering approaches to robotics?
• What is the difference between telepresence and
semi-autonomous control?
What is intelligent robots?
• Mechanical creatures that can function
autonomously, which means it can sense,
act, maybe even reason; doesn’t just do
the same thing over and over like
automation.
• The intelligent robots arose by the
development of AI since the 1990’s.
Teleoperation
• Teleoperation is that a human operator controls a robot
from a distance.
• It is a ideal solution for controlling remotes because AI
technology is nowhere near human levels of competence,
especially in terms of perception and decision making.
• Cons: Cognitive fatigues; communications dropout;
communications bandwidth; communications lag;
Add more intelligence to the
early teleoperation
• Telepresence
– providing sensory feedback to the point that teleoperator
feels they are “present” in robot’s environment by adding
more cameras.
• Semi-autonomous control
– human is involved, but routine or “safe” portions of the task
are handled autonomously by the robot
– It is really a type of mixed-initiative
The Seven Areas of AI
• knowledge representation
– How does the robot represent its world, task, and itself.
• understanding natural language
– Natural language is usually challenging, it is not only talking
about looking up words from a dictionary by understanding.
• Learning
– A robot could be programmed by just watching a human’s
behaviors.
The Seven Areas of AI
• planning and problem solving
– The ability to plan actions and solve problems with those
plans
• Inference
– Inference is generating an answer when there is no complete
information
• Search
– Search means efficiently examining a knowledge
representation of a problem to find the answer.
• Vision
– The robot can simulate the effects of actions in its “head”
Robotics Paradigms
• What are robotic paradigms?
– A paradigm is a philosophy or set of assumption
and/or techniques which characterize an approach to
a class of problems.
• There paradigms:
– Hierarchical paradigm (Ch. 2)
– Reactive paradigm (Ch. 4)
– Hybrid paradigm (Ch. 7)
Ch. 2: Hierarchical paradigm
• The oldest paradigm, and was prevalent from
1967-1990.
• Under this paradigm, the robot senses the world,
plans the next action, and then acts.
SENSE
PLAN
ACT
Strips: means-ends analysis
•
•
Strips is a variant of the general problem solver method, it uses
an approach of means-ends analysis, where if the robot can’t
accomplish the task in one “movement”, it picks a action which
will reduce the difference between what the now state versus
the goal state.
To implement Strips, Designer must set up
–
–
–
World model representation
Difference table with operators, preconditions, add & delete lists
Difference evaluator
Strips: means-ends analysis
• Strips assumes closed world
– Closed world: world model contains everything
needed for robot (implication is that it doesn’t change)
– Open world: world is dynamic and world model may
not be complete
• Strips suffers from frame problem
– Frame problem: representation grows too large to
reasonably operate over
Representative Architecture
• An architecture is a method of implementing a
paradigm, of embodying the principles in some
concrete way.
• The two best known architectures are the
Nested Hierarchical Controller (NHC)
developed by Meystel and the NIST Realtime
Control System (RCS) originally developed by
Albus.
Evaluating the Two
Architectures
•
support for modularity:
– decomposition by functionality
•
niche targetability:
– good, both have been used for apps like vehicle guidance, mining equipment
•
ease of portability to other domains:
– unclear, not sure if code could be reused—lots of rewriting on previous apps
•
robustness:
– RCA simulates plans in advance, but not sure what it would do with sensor or
mechanical failures, etc.
Advantages and
Disadvantages
• Advantages:
– It provides an ordering of the relationship between
sensing, planning, and acting.
• Disadvantages:
– Planning: for every update cycle, robots had to do
some type of planning.
– Dependence on a global world model
– Uncertainty: did the robots actually finish the action?
We don’t know for sure.
Ch. 3: Biological Foundations of
the Reactive Paradigm
• Why explore the biological sciences?
• What are the three levels in a computational
theory?
• What are animal behaviors?
• Coordination of behaviors, perception, schema
theory, and more…
Why do we need to explore the
biological sciences?
• Animals and man provide existence proofs of
different aspects of intelligence.
• The principles of animal intelligence are
extremely important.
– For examples: roboticists may overcome the closed
world assumption that presented problems with
shakey by observing the animals behaviors in an
open world.
Marr’s Computational
Theory
• The levels in the computation theory can be
stated as:
• Level 1: What is the phenomena we’re trying to
represent?
• Level 2: How it be represented as a process with
inputs/outputs?
• Level 3: How is it implemented?
Animal Behaviors
• A behavior is a mapping of sensory inputs to a pattern of
motor actions which then are used to finish a task
• Three catagories:
– Reflexive
• stimulus-response, often abbreviated S-R
– Reactive
• learned or “muscle memory”
– Conscious
• deliberately stringing together
Coordination and Control of
Behaviors
• There are four ways to acquire a behavior, which are:
• To be born with a behavior (innate)
– Examples: Arctic terns.
• To be born with a sequence of innate behaviors.
– Examples: mating cycle in digger wasps.
• To be born with behaviors that need some initialization (innate with
memory).
– Examples: bees, which are born with in hives.
• To learn a set of behaviors
– Examples: Lions, who are nor born with any hunting behaviors.
How behaviors are coordinated and controlled
-- innate releasing mechanisms (IRM)
Releaser
Pattern
of Motor
Actions
Sensory
Input
BEHAVIOR
• The Releaser acts as a control signal to
activate a behavior. If a behavior is not
released, it does not respond to sensory
inputs.
Perception
World
Acts &
Modifies
World
Cognitive
Activity
Samples, Finds
Potential Actions
Directs what
to look for
Perception
of
Environment
• Two functions of perception (can be the same
percept)
– Release a behavior
– Guide a behavior
• Action-oriented perception (Neisser)
– Planning is not needed to act
– Perception is selective
Schema Theory
• Schema theory provides a helpful way of casting
some of the insights from above into an OOP format.
• is generic, equivalent to an object in OOP
– schema specific knowledge (local data)
– procedural knowledge (methods)
• schema intiantation is specific to a situation, equivalent
to an instance in OOP
• a behavior is a schema, consists of
– perceptual schema
– motor schema
Ch. 3: Summary
• A behavior is the fundamental element of biological
intelligence, and will server as the fundamental
component of intelligence in most robot systems.
• Innate Releasing Mechanisms (IRM) are one model of
how intelligence is organized.
• Perception in behaviors serves two roles, including a
releaser for a behavior and a precept which guides the
behavior.
• Schema theory is an object-oriented way of representing
and thinking about behaviors.
Ch. 4: The Reactive Paradigm
• The Reactive Paradigm was a reaction to the
Hierarchical Paradigm, and it was heavily used
between 1988-1992.
• The fast execution time can be achieved by
throwing away “Planning”.
RELEASER
SENSE
behavior
ACT
Reactive Robots
RELEASER
SENSE
behavior
ACT
• Most apps are programmed with this paradigm
• Biologically based:
– Behaviors (independent processes), released by perceptual or
internal events (state)
– No world models or long term memory
– Highly modular, generic
– Overall behavior emerges
Hierarchical Organization is
“Horizontal”
• Horizontal decomposition of tasks into the S, P, A
organization of the Hierarchical Paradigm.
More Biological is “Vertical”
• The right figure
shows that a vertical
decomposition of
tasks into an S-A
orgrnization.
Architectures
• Historically, there are two main styles of creating
a reactive system:
– Subsumption architecture
• Layers of behavioral competence
• How to control relationships
– Potential fields
• Concurrent behaviors
• How to navigate
Subsumption Architecture
• Subsumption has a loose definition of behavior as a tight coupling
of sensing and acting.
• Higher layes may subsume and inhibit behaviors in lower layers.
• The design of layers and their behaviors is usually difficult.
• Behaviors are released by the presence of stimulus.
• Subsumption solves the frame problem by eliminating the need to
model the world because the behaviors just simply respond to
whatever stimulus is in the environment.
• Perception is largely direct, using affordances.
• Perception is ego-centric and distributed.
Potential Fields
• Potential field styles of behaviors always use vectors to
represent behaviors and vector summation to combine vectors
from different behaviors to produce an emergent behavior.
• Behaviors are defined as consisting of one or more of both
motor and perceptual schemas and (or) behaviors.
• All behaviors operate concurrently and output vectors are
summed.
• Behaviors may make varying contributions to the overall action
of the robot, although they are treated equally.
• Perception is usually handled by direct perception or
affordances.
• Perception can be shared by multiple behaviors.
Evaluation of Reactive
Architectures
• Support for modularity
– Both decompose the actions and perceptions. Subsumption favors a
composition suited for a hardware implementation, whereas potential
fields methods for a software-oriented system.
• Niche targetability
– Both have hign targetabilities.
• Ease of portability to other domains
– Subsumption depends on low layers heavily, while potential fields
usually have no implicit reliance on a low layer.
• Robustness
– Neither can be called genuinely robust.
Ch. 4: Summary
• The organization of the Reactive Poradigm is SENSEACT, No PLAN component.
• Under reactive paradigm, behaviors serve as the basic
building blocks for robot actions.
• Reactive systems also exhibit good software engineering
principles due to the programming by behavior approach.
• At last, two representative architectures are
subsumption and potential fields. However, despite the
differences in theory, these two systems appear to be
largely equivalent practically.
The key points to understand what
is main characters of
AI robotics?
OOP (Object-Oriented Programming)
Model of sensing
Hybrid deliberative/Reactive Paradigm
Example of our homework#3
Future of Robot
What is OOP?
Object-Oriented Concepts tap into this natural human
tendency resulting in an easy to understand and use
language.
An automobile is a very good example of the ObjectOriented Concept. As humans, it is our natural tendency
to think of an automobile as a single "thing", and not as a
large group of several thousand small "things". Thinking
of the automobile as a single "thing" helps us deal with
the overwhelming complexity of the whole machine. We
would say simple statements like; "Fill her up.“ or "How
fast are we going?" or "I have a Blue car. " ..... and
everyone would understand how those statements apply
to our car.
1. Example for OOP Programming
Using an automobile as an example of an Object, the following program
shows an example of Object Oriented programming:
BobsCar.Speed = 50
If BobsCar.Speed>CurrentRoad.SpeedLimit Then
PoliceCar.Mode = Chase
PoliceCar.Target = BobsCar
PoliceCar.Speed = BobsCar.Speed + 10
End If
Is it very simple and easy to understand?
Here, please imagine that if we do not use OOP, what should our
program look like?
2. How behaviors can be implemented
using OOP constructs such as classes?
Recall from software engineering that an object consists of
data and method, also called attributes and operations.
And as noted before, schemas contain specific knowledge
and local data structures and other schemas. So, a schema
as a programming object will be a class. It’s defined as
below:
3. Example: move-to-go behavior
1) We put a robot in an empty arena with Coca-cola cans in
random location and a blue recycling bin in a corner.
2) The behaviors needed is picking up a red can and moving
to a blue bin. But we write a single generic behavior
move_to_goal (color) to deal with both behaviors.
3)The behavior move_to_goal consist of a perceptual schema,
which will be called extract-goal and a motor schema,
which used an attractive field. extract-goal uses the
affordance of color to extract where the goal is in the
image, and then computer the angle to the center of the
colored region and size of the region.
The table below implies some important points about programming
with behaviors:
4) The attraction motor schema takes that
percept and is responsible for using it to
turn the robot to center on the region and
move forward.
Object
Behavioral Analog Identifier
Data
Percept
goal_angle
goal_strength
Method Perceptual_schema extract_goal(goal_color)
Motor_schema
Pfield.attraction
(goal_angle, goal_color)
5) Two schemas are both independent. The perceptual
schema doesn’t know the existence of motor schema.
1. Model of sensing
environment
Sensor
Observation
Or Image
Percept
Perceptual
Schema
Robot
Action
Motor
Schema
Sensor/transducer---------->Behavior------------->Action
2. Behavioral Sensor Fusion
Sensor
Sensor
Fusion
Behavior
Sensor
Perception in a reactive robot system has two roles:
1)to release a behavior
2)to support or guide the action of the behavior
All sensing is behavior-specific, where behaviors
map tap into the same sensors, but use the data
independently of each other.
The Hybrid Deliberative /Reactive
Paradigm
1. It can be thought as PLAN, then SENSE—ACT.
2. The SENCE—ACT portion is always done with
reactive behaviors, where PLAN includes a broader
range of intelligent activities.
3. Planning can be interviewed with execution.
4. Architecture usually encapsulate functionality into
modules. The basic modules are: mission planner,
behavior manager, performance monitor.
5. State-hierarchies divide deliberation and reaction by
the state, available to the modules or agents operating
that layer. Three states are: Past, Present, Future.
Example
Plan (the Algorithm we use)
ś=f (s(i));δ=g (Ψ(s), Ψd(s));
s(i+1)=h (s(i));
Xd=f1(s(i)); Yd=f2(s(i))
Sense (Virtual Vehicle)
Xd(s), Yd(s), Ψ(s), Ψd(s)
ACT (Actual Robot)
X(s+1), Y(s+1), Ψ(s+1), Ψd(s+1)
Do we use PLAN—SENSE—ACT concept?
Modules concept? State-hierarchies?
Planning can be interviewed with execution?
Future of Robot
Enabling technologies
Enabling technologies ranging from sensors to radio
communications and navigation aids are all accelerating
logarithmically. The ubiquitous acceptance of wireless
LAN systems, the plunging costs of video cameras and
processors, the availability of affordable laser navigation
systems, and the ever-increasing accuracy and dropping
cost of GPS navigation receivers are all combining to
make autonomous robots potentially cheaper and ever
more capable.
At least as important, we now have enormous resources
in human experience. Countless software engineers and
academics have spent endless hours developing concepts
of modeling and control that are just as much part of the existing robotics
toolbox as any sensor or processor. As a result, only the integration of
these elements is required for new robotic configurations to burst onto the
scene with blinding speed.
Social forces
The social issues already discussed are pushing customers to look for new
solutions to performing many of the tasks that now require manual labor.
These are tasks which autonomous robots can easily provide. Slowly but
surely, a few venture capitalists (real ones) are beginning to make
investments in companies like iRobot, and the industry is beginning to
gain a little attention.
Thank you for your time!