Transcript Introduction to Robotics Class

Short
History of
Robotics
Pre-History of Real-World
Robots:
• One of the first robots was the clepsydra or
water clock, which was made in 250 B.C.
• It was created by Ctesibius of Alexandria, a
Greek physicist and inventor.
• Hero from Alexandria built robot theater
Jacques de Vaucanson
(1709-1782)
• Master toy maker who won the
heart of Europe.
• Flair for inventing the
mechanical revealed itself
early in life.
• He was impressed by the
uniform motion of the
pendulum of the clock in his
parents hall.
• Soon he was making his own
clock movements.
James Watt
Mechanical horse
Pre-History of Real-World
Robots:
• The earliest remote control vehicles were built
by Nikola Tesla in the 1890's.
• Tesla is best known as the inventor of AC
power, induction motors, Tesla coils, and other
electrical devices.
Robots
of the
media
Popular culture influenced by these
ideas
History of Robotics?
 RUR
 Metropolis(1927)
 Forbidden planet(1956)
 2001 A Space Odyssey(1968)
 Logans Run(1976)
 Aliens(1986)
Robot
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Let us step down fantasies…..
“Tortoise”
...
1951
• Other early robots (1940's - 50's) were Grey
Walter’s “Elsie the Tortoise” ("Machina
speculatrix") and the Johns Hopkins "beast."
History of
RealWorld
Robots:
Grey Walter's tortoise, restored recently by Owen Holland
and fully operational
What are robots made of?
•Sensors: Light Sensors
Grey Walter’s Tortoise
Isaac Asimov and Joe
Engleberger
• Two fathers
of robotics
• Engleberger
built first
robotic arms
History of Real-World Robots:
• The first modern industrial robots were
probably the “UNIMATES”, created by
George Devol and Joe Engelberger in the
1950's and 60's.
• Engleberger started the first robotics
company, called "Unimation", and has been
called the "father of robotics."
•
Unimates, late 50’s to early 60’s
•
Automotive Industry
•
Recent Recovery
•
Worth over $500 million
•
99% are industrial
The Advent of Industrial Robots -
Robot Arms
• There is a lot of motivation to use robots
to perform task which would otherwise
be performed by humans:
– Safety
– Efficiency
– Reliability
– Worker Redeployment
– Cheaper
What are they?
• Most of the industrial robots used in factories
throughout the world exhibit few of the
characteristics that the average person would
associate with the term "robot"
• Many are simple "pick and place" machines
• Lets have a working definition for an
industrial robot
• These machines are programmable, are
automatic and can perform a wide variety of
tasks
Robot Manipulators
Pick and Place Machines
(robots?)
• Simplest kind of industrial robot
• Still some on production lines but are being
phased out
• Perform simple pickup and drop functions
• Cannot sense environment
• The limits of motion of each joint of the machine
are fixed by electric or pneumatic impulse
originating at a plug-board control panel
Servo Robots
• A more sophisticated level of control can be
achieved by adding servomechanisms that can
command the position of each joint.
• The measured positions are compared with
commanded positions, and any differences are
corrected by signals sent to the appropriate
joint actuators.
• This can be quite complicated
What are robots made of?
•Effectors:
Manipulation
Degrees of Freedom
Degrees of Freedom
• For the robot arms to become more flexible,
more "degrees of freedom" or planes of free
movement had to be added.
• Many industrial arms have 6 or more planes
of motion
Teach and Play-back Robots
• Once the math is solved it is a relatively simple
matter to teach a robot how to pick something
up and what to do with it.
• Teaching involves moving the arm through the
motions it is expected to perform
• During Recall mode the arm repeats,
verbatim, what it has been taught
Teach and Play-back Robots
An Arm's Simple World
• Life is simpler for a robot arm which can
always expect objects to be oriented in
the same way.
• It only has to worry about its own
coordinate system.
• The math gets complex but is manageable
The U.S. military
contracted the "walking
truck" to be built by the
General Electric
Company for the U.S.
Army in 1969.
Walking robots
History of Real-World
Robots:
• The walking truck was the first legged vehicle
with a computer-brain, developed by Ralph
Moser at General Electric Corp. in the 1960s.
• It was a large (3,000 pounds) four legged robot
that could walk up to four miles a hour.
The Army and the
Artificial Elephant
General Electric Walking Truck.
–A human controlled the stepping of this
robot by pushing pedals with his feet.
–The complicated coordination of
movements within a leg and between
different legs during stepping was
controlled by a computer
The Army and the Artificial Elephant
• Project failed because of the
"unanticipated computational difficulty"
of simultaneously controlling all of the
degrees of freedom in the four legs.
• This failure dramatically demonstrated the
sophistication that control systems must
have to produce successful walking
behavior in legged mechanisms.
Marvin
Minsky
• MIT Pioneer
of AI and
Robotics
Robotics and AI are very
new research areas, most
pioneers are alive and well.
Over Confidence
• Soon people had faith in their own ability
to solve what turned out to be extremely
complex control problems
A Robot's More Complex World
• It gets more complex when you expect an arm
to pick up objects which can be in any
orientation.
• There are several problems
–
–
–
–
How do you pick it up?
How do you recognize it is there?
How do you know you are holding it firmly?
How do you have to change your grip to hold it the
way you need to?
• This is still a subject of much research
Expanding Horizons
• Undaunted by previous failures, robotocists
continued research in the field
• People thought a good strategy would be:
– to start from the state-of-the-art as practiced in
industrial robotics
– and gradually expand the sensory and control
capabilities
until the more difficult tasks became tractable.
• This was the strategy adopted by the robotics
group at S.R.I
History of Real-World
Robots:
• “Shakey" was a small unstable box on wheels
that used memory and logical reasoning to solve
problems and navigate in its environment.
• It was developed by the Stanford Research
Institute (SRI) in Palo Alto, California in the
1960s.
ShakeyShakey
of Nilsson
Nils Nilsson @ Stanford Research Inst.
first “general-purpose” mobile platform
...
...
1968
Shakey the
robot.
Shakey was the first
mobile robot that
could think
independently
and interact with
its surroundings
• Conceived as a demonstration
project for the Advanced
Research Projects Agency
(ARPA) artificial Intelligence
program
Shakey (cont)
• Shakey could be given a task such as finding a box of a given
size, shape, and color and told to move it to a designated
position.
• Shakey was able to search for the box in various rooms, cope
with obstacles, and plan a suitable course of action.
• It was controlled by an off-board PDP-10 computer through a
radio link.
• It carried :
– a TV camera,
– an optical range finder,
– and touch sensors
so that it could know when it bumped into something.
Shakey (cont)
Shakey (cont)
• While Shakey was a success in some respects it
was a great failure as far as autonomy was
concerned...
• It was controlled by an off-board computer
• It could only detect the baseboards of the
special rooms it worked in
• It could not deal with an unconstrained
environment
• It was really slow!
Shakey
Shakey
(cont)
Nils Nilsson @ Stanford Research Inst.
first “general-purpose” mobile platform
Living Room (L)
Kitchen (K)
sp
sh
tv
tvg
Bedroom (B)
...
...
1968
Shakey Start
Shakey
(cont)
START
At(sh,L)  At(sp,K)  At(tvg,B)  At(tv,L)
ACTIONS
• Go(x,y)
Preconditions: At(sh,x)
Go(L,B)
Push(tv,L,B)
Go(L,K)
Postconditions: At(sh,y)
Push(tv,L,K)
At(sh,K)  At(sp,K) 
At(tvg,B)  At(tv,K)
• Push(obj,x,y)
Preconditions: At(sh,x)  At(obj,x)
Postconditions: At(sh,y)  At(obj,y)
At(sh,L)  At(sp,L)  At(tvg,L)  At(tv,L)
GOAL
...
ACTING
motor control
task execution
planning
world modeling
“functional” task decomposition
perception
Hans Moravec @ SAIL
SENSING
Stanford
Cart
Stanford Cart
...
1976
AI - Historical
Perspective
• Artificial Intelligence began with very ambitious goals in the 1950s & 60s
• Most initial work on AI focused on severely abstracted “toy problems”
• Recent work (mid-80s to present) has been very successful in finding
applications that are firmly grounded in the real world
–
–
–
–
Intelligent assistants
Computer vision for navigation, graphics
Robotics systems for manufacturing
Speech analysis & generation
• Basic observations:
– Artificial Intelligence has specialized into many inter-related but distinct
disciplines
– Tasks performed effortlessly by humans & animals often are the hardest to
emulate
History of AI
•
–
–
• McCulloch and Pitts (1943)
– Neural networks that learn
• Minsky (1951)
– Built a neural net computer
• Darmouth conference (1956):
1947~1959
•
cybernetics 1947
Dartmouth 1956
1960~1964
–
–
LISP(1960)
GPS(1963)
– McCarthy, Minsky, Newell, Simon met,
– Logic theorist (LT)- proves a theorem in Principia Mathematica-Russel.
– The name “Artficial Intelligence” was coined.
• 1952-1969
–
–
–
–
–
–
GPS- Newell and Simon
Geometry theorem prover - Gelernter (1959)
Samuel Checkers that learns (1952)
McCarthy - Lisp (1958), Advice Taker, Robinson’s resolution
Microworlds: Integration, block-worlds.
1962- the perceptron convergence (Rosenblatt)
History of AI, continued
• 1966-1974 a dose of reality
– Problems with computation
• 1969-1979 Knowledge-based systems
– Weak vs. strong methods
– Prolog(1973)
– Expert systems:
• Dendral : Inferring molecular structures
• Mycin: diagnosing blood infections
• Prospector: recomending exploratory drilling (Duda).
EasyFinder
Excite Live
FarCast
(Electronic Commerce)
Mysimon
Amazon
CDNow
eWatch
Careersite
Intelligent Miner
– Roger Shank: no syntax only semantics
• 1980-1988: AI becomes am industry
– R1: McDermott, 1982, order configurations of computer systems
– 1981: Fifth generation
• 1986-present: return to neural networks
• Recent event:
– Hidden Markov models, planning, belief network
Key Schools of Thought in
AI and Robotics now
•1. Symbolic AI
– The physical symbol hypothesis
(Newell & Simon, 1976)
•A physical symbol system has the necessary & sufficient means for general intelligent action
•2. Sub-symbolic AI
– Connectionist/neural net approaches
•Rely on signals, not symbols
Intelligence emerges from connections between entities in an evolving dynamical system
– The physical grounding hypothesis
(Brooks, 1990)
•Behavior modules of an agent interact with the environment to produce complex behavior
without using centralized symbolic models
Rodney A. Brooks
• Born in Adelaide, Australia in 1954
• Received Ph.D in computer science from
Stanford University
• Member of the M.I.T Artificial Intelligence
Lab where he leads the mobile robot group.
• Well funded to do research in autonomous
vehicles. ($$$$)
The early years
• Brooks was painfully aware of the failure
of robotics to live up to its potential.
• Autonomous vehicles were not that
autonomous and weren't even very good
vehicles.
• He identified various aspects of mobile
robotics which he considered to be
important and obvious
Brook's Robot Requirements
• He identified a number of requirements of a control
system for an intelligent autonomous mobile robot.
• Multiple Goals:
– Some conflict, context dependent
• Multiple Sensors:
– All have errors, inconsistencies and contradiction.
• Robustness:
– The robot must by fault-tolerant.
• Extensible:
– You have to be able to build on whatever you built
Brook’s Dogma
• Brooks also introduced, what he called,
"9 dogmatic principles",
– 1) Complex (and useful) behavior need not
necessarily be a product of an extremely complex
control system.
– 2) Things should be simple: Interfaces to
subsystems etc.
– 3) Build cheap robots that work in human
environments
– 4) The world is three-dimensional therefore a
robot must model the world in 3 dimensions.
Brook’s Dogma
Dogma (cont)
• 5) Absolute coordinate systems for a robot are the
source of large cumulative errors.
• 6) The worlds where mobile robots will do useful
work are not constructed of exact simple polyhedra.
• 7) Visual data is useful for high level tasks. Sonar
may only be good for low level tasks where rich
environmental descriptions are unnecessary.
• 8) The robot must be able to perform when one or
more of its sensors fails or starts giving erroneous
readings.
Brook’s Dogma
• 9) "We are interested in building
"artificial beings"
– --robots that survive for days, weeks and
months,
• without human assistance,
• in a dynamic complex environment.
– Such robots must be self-sustaining
Subsumption
• Brooks and his group eventually came up with a
computational architecture.
• Model arrived at by continually refining attempts to
program a robot to reactively avoid collisions in a
people-populated environment.
• Not intended as a realistic model of how neurological
systems work.
• The model is called "subsumption architecture”.
• Its purpose is to program
– intelligent,
– situated,
– embodied agents.
Subsumption Priciples
• 1) Computation is organized as an asynchronous network of
active computational elements:
– they are augmented finite state machines,
– with a fixed topology of unidirectional connections.
• 2) Messages sent over connections have no implicit
semantics:
– they are small numbers (typically 8 or 16 bits, but on some robots
just 1 bit),
– their meanings are dependent on the dynamics designed into both the
sender and receiver
• 3) Sensors and actuators are connected to this network,
– usually through asynchronous two-sided buffers.
Allen
• First Subsumptive Robot
• Almost entirely reactive, using sonar
readings to keep away from people and
other moving obstacles, while not
colliding with static obstacles.
• Also had a non-reactive higher level
which attempted to head towards a goal.
• Used same type of architecture for both
types of behaviors.
Herbert
• Used a laser scanner to find soda can-like objects
visually,
– infrared proximity sensors to navigate by following
walls and going through doors.
• A magnetic compass was used to maintain a
global sense of orientation.
• A host of sensors on an arm were used to reliably
pick up a soda can.
• Herbert's task was to wander around looking for
soda cans, pick one up and bring it back to where
Herbert had started from.
Where we’re at.
• Inch square
robots.
• Food
scavengers.
• Play Tag.
• Find shade.
Squirt
• Smallest robot they built
• Weighs 50 grams and is about 5/4 cubic
inches in volume.
Squirt (cont)
Squirt
• Incorporates an 8-bit computer, an on-board
power supply, three sensors and a propulsion
system.
• Normal mode of operation is to act as a "bug",
hiding in dark corners and venturing out in the
direction of noises, only after the noises are long
gone.
• The entire compiled control system for Squirt
fits in 1300 bytes of code on an on-board
computer.
Genghis
• Genghis is a 1Kg six legged robot which walks
under subsumption control and has an
extremely distributed control system
• It can walk over rough terraine using 12
motors, 12 force sensors, 6 pyroelectric
sensors, one inclinometer and 2 whiskers.
• They built a follow-up, Attila--Stronger
climber, and able to scramble at around 3
KPH.
Genghis (cont)
Genghis
From Genghis Insects
to Artificial Insects
Rodney Brooks @ MIT
identify objects
build maps
explore
ACTING
SENSING
“behavioral” task decomposition
planning and reasoning
wander
avoid objects
...
...
1985
Cartland
Where do we go from
here?
• There are many many problems that
subsumption does not address, including
adaptation through learning
• There is still much work to be done and it
doesn't have to cost that much money.
• Try building one of these yourself.
• 1.In this class you will
learn programming
language Lisp - especially
suited for robotics. You
• 3. Many of above ideas
should already know
were programmed by us in
Basic of C. Think how
Basic. The Visual Basic
would you program robots
environment from
with behaviors described
Microsoft allows for
above.
interfacing, voice
• 2. If you feel your
recognition and synthesis
knowledge of
and robot vision. If you are
programming is
interested in these areas, try
insufficient, start learning
to review Visual Basic now.
LISP now.
It is located on PCs in the
lab.
Problems
Sources
•
•
•
•
Padhraic Smyth
Kiriakos Kutulakos, University of Rochester
Rojas FUB MI
Behnke
• A. Ferworn
• Dodd, Harvey Mudd College
• Internet
• Brian Glassman, Mechanical Engineering at Florida Institute of Technology
• John Gallagher, SUNY Institute of Technology