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ABSTRACT
The Stanford Artificial Intelligence Laboratory proposes to undertake
a program in advanced automation over a period of one year at a cost
of $200,000. The laboratory proposes a three or four year
investigation, with one year funding, in the area of computer
integrated assembly systems. This project unites two dominant
forces in industrial automation: computerization of manufacturing
and programmable assembly automation.
Extensive industrial and governmental studies and consultation have
guided the concepts of this proposal. Several industrial needs have
emerged from studies now underway. One is a widespread desire of
major industries for programmable automation. Another is the desire
to cut costs by better overall organization and understanding of the
manufacturing process, using computers as a tool for organization and
information gathering.
The proposed project would study assembly systems from two
directions. The first is a "top-down" study including problem
definition, characterization of descriptors of objects and assembly
operations, automated assembly planning, simulation to aid design for
assembly, and APT-like language design for assembly. The second is
an evolutionary and constructive effort based on demonstrated
abilities, adding incrementally to our existing system. We feel that
our system and proposed extensions are valuable examples for systems
useful in industry. We maintain that any system study must be based
on a planned series of experiments which demonstrate the feasibility
of key modules in the system, and the adequacy of the combined
system.
TABLE OF CONTENTS
I NATIONAL AND SOCIETAL NEEDS
II RESEARCH PLAN
II.A THE ASSEMBLY SYSTEM
II.B WHAT HAS BEEN DONE
II.C WHAT WE PROPOSE TO DO
III MANAGEMENT PLAN
III.A SCHEDULE
III.B MILESTONES
IV RELATED PROGRAMS AT THIS LABORATORY
IV.A PAST RESEARCH IN VISION
IV.B CONTINUING VISION RESEARCH
IV.C MANIPULATOR DEVELOPMENT
IV.D SYSTEM REQUIREMENTS
V RELATED PROGRAMS IN OTHER ORGANIZATIONS
VI UTILIZATION PLAN-DISSEMINATION OF RESULTS
VII APPENDICES
VIII VITAE AND BIBLIOGRAPHIES
IX CURRENT SUPPORT
BUDGET
REFERENCES
I. NATIONAL AND SOCIETAL NEEDS
The contributions that advanced automation can make to enhance
productivity in manufacturing are now widely appreciated. Current
interest in advanced computer-oriented automation stems from two
sources. The first is the decreasing cost of computation combined
with an improved understanding of how to apply computation to various
aspects of manufacturing. The second cause is the realization that
many current manufacturing operations, primarily assembly and
inspection, will require complex automation methods which are now
research techniques. The same considerations apply to the automation
of tasks such as laboratory and medical procedures, but the emphasis
has been (as ours will be) on manufacturing. A recent ARPA-sponsored
study [Anderson 1972] in which we participated suggests the following
major benefits from advanced automation:
improved national productivity
reduced prices
increased product quality and uniformity
customization of products
increased job satisfaction.
We would like to add just one comment. As we have seen with other
technological developments such as numerical-controlled tools, there
will be things made which were previously impractical to make. There
is widespread agreement in the technical, industrial and government
communities on the potential benefits of advanced automation. There
is much less agreement on the level of sophistication needed in
automation hardware and software. While there are large application
areas in which specially constructed machines or simple general
purpose devices will prove optimal, we believe that the most
practical solution for intermediate quantities (500 to 100,000) will
be to develop general purpose assembly and inspection devices
controlled by sophisticated programs in digital computers.
These conclusions are supported by our consultation with industrial
management who have manufacturing responsibilities. Programmibility
is necessary even in the automobile industry which has large volume,
but essentially custom assembles each car. This has been repeatedly
emphasized in workshops and private conversations.
Another conclusion is that the process of improving the organization
of manufacturing can make heavy use of computer systems.
Computer-aided design is already in heavy use in semiconductor
industries, and making a beginning in other industries. Inventory
and parts tracking are important facilities in the aircraft industry.
Direct numerical control of several N/C machines by a central
computer is also extensively used. Some effort has been directed in
Europe towards the "group technology" approach of Opits, which
attempts to emphasize commonality by using parts descriptions to
group items which are alike. Evolution of computer-aided
manufacturing depends on integrating these facilities and bringing
more facilities under effective control.
These two systems, programmable automation and computer-aided
manufacturing,interact in a beneficial way. An important part of the
development process for programmable automation is the development of
programming systems which actually program assembly devices. Part of
that can be done by "learning by doing". However, many operations
are only small modifications of other operations, so that it is
desirable to be able to modify the assembly program instead of
reprogramming entirely. It would appear that most assembly
operations could be strung together from modifications of modules
from a library of standard assembly subroutines like inserting and
screwing. Inspection combined with assembly (which is made necessary
for quality control and because of the implicit inspection in
assembly operations) and error and contingency conditions lead to an
assembly control structure like a conventional computer program.
The central computer will have parts designs and assembly operations.
Why should these not be used to supply information to assembly
subroutines? And should the designer have a system which allows him
to design for automatic assembly by interacting with the system which
programs the actual assembly device? That would provide a useful
experimental system for cost and design automation, in addition to
shifting some of the responsibility for programming of assembly
devices from assembly labor to professional staff with technical
sophistication. We emphasize the separation into an assembly system
which could operate stand-alone, but is better interfaced with a
central computer system, and the robot programming system which
depends on more facilities but is used infrequently when jobs are
changed.
There is one further concept in common among presentations of
automation. Automation must be incremental. This is supported by
numerous considerations: invested capital in existing plants, design
costs for conversion, and the necessity for a sequence of
understandable and predictably profitable steps along the path.
We contend that inserting programmable assembly devices incrementally
into existing factories or new factories with only slight
modifications, demands more from the programmable device. We see
this process at work as industrial robots of the Unimate class slowly
develop more facilities. The simple robot described in the ISI study
[Anderson] does not exist yet; it must still be proven feasible by
demonstration. We outline below some arguments about cost and
productivity factors which follow from programmable assembly. These
arguments follow from compatibility with current manufacturing
design, from economies of scale, and from commonality of technology,
and from desires for quality of product and predictability of the
process. Several studies have indicated some priorities among
directions of automation. The ISI study [Anderson] indicates that
less than 20% of discrete manufacturing is amenable to special
purpose automation. A Chrysler Corporation study indicates that car
assembly labor has first priority in automation; assembly plant
direct labor is 40% of direct corporate labor. The ISI study quotes
a figure of 75% as the fraction of batch produced discrete
manufactures. One conclusion is that "the emphasis in development
should be on simple vision devices and on low-bandwidth tactile and
force sensing feedback". That study indicates that the assembly
process typically accounts for 50 percent of production cost.
We assume that present manipulators have a small but stable market,
and that most will have minicomputers soon (Sundstrand does now).
Will the incremental cost of touch, force and visual sensors be
offset by the opening of new capabilities, and will the cost be
offset by reduced cost of system design for machines which allow
loose alignment tolerances? We estimate that touch and force sensing
will add about 5% to the price of current manipulators of the Unimate
class. We estimate that by 1977, the incremental cost of visual
sensors, a laser ranging device such as we use now,and additional
memory requirements, will add about 15%-20% to current manipulator
prices. If labor costs continue to rise, part of the increase will be
offset, but cost comparisons will not change appreciably over that
time scale unless significant new mechanical designs are made. A
conclusion is that sensor-equipped robots will be available at slight
additional cost. We will discuss the benefits of incremental
improvements to manipulators.
We do not expect mechanical production costs to drop as dramatically
as electronics costs fall with integration. The most promising cost
reduction development would be high-production standard modules such
as robots. As an example, consider the Stanford arm; produced in
unit quantity, parts, manufacturing and assembly costs are
approximately $10K. The arm is much simpler than an automobile; there
is no reason why it should cost more. A smaller, more recent design
costs $3500 each in quantities of two.
At what cost are robots economically viable? This varies enormously;
if they are serving a large and expensive machine, the cost can be
relatively high. Unimation now projects labor costs at $22,000 to
$24,000 per man year averaged over the next 10 years, and assumes a
payoff of 2 years for a projected acceptable cost level of $46,000. A
robot with two arms, minicomputer control, force and touch sensors
could be constructed for under $10K in quantities of 1000 now. To
summarize: two arms (new small Scheinman design) $2000 with hand; arm
drivers and arm interface electronics $1000; force and touch $800;
small computer 8k memory $4500; total $8300. Assuming that the
computer price falls to $1200 by 1978, this will at least compensate
for inflation. Of course, these are small arms, suited for parts
assembly, not automotive body assembly. The larger arms, assuming
slight redesign for cost, would cost about $2500 in units of 1000,
for a total system cost (not price) of $12300. This is the range of
price of present transfer machines which have limited market because
they have limited motion, no control, and are thus locked out of the
economy of scale. There is a dynamic which leads to economy of scale;
the greater the versatility, the wider is the market for
applications. This will be true particularly in small production and
job shop operations which have been difficult to automate. The
larger the production, the more versatility for the same initial cost
as special devices. Also, with high volume, standardization of
maintenance, and availability of parts and service are improved. The
quality of the device is also improved.
The economies of scale in production of assembly modules are matched
by economies of intellectual scale in development of design for
automation. The design process is modularized and easily
communicable; there is a common discipline which can be widely
absorbed; developments of many sources can be integrated. Programming
and training done once for one machine can be transferred to all
machines in a plant, and even transferred nationwide. Functional
modularity has been emphasized in our studies. Modularity is clearly
possible and there is a powerful economic incentive to assemble
widely varying systems from one, two, or three arm modules, force and
touch modules, vision and ranging modules, with planning and
monitoring modules. This would also entail software modularity with
computer-aided mechanical design and job specification for robots on
a local or network time-sharing service.
The alternative is special design for each operation. We expect that
at some point, design will be cheaper with standard robot modules
than with special purpose designs, just as it is now more economical
to use mini-computer controllers than special purpose controllers.
Modularity has the potential to reduce design time and improve
predictability of design time and costs, permitting better planning
and response to competition. To some extent, the dehumanized
industrial worker now fills the role of standard module, so that we
do not expect discontinuous change. The continuity with decades of
experience with design for human versatility in assembly can be
relied upon; robots with some versatility, sensing, contingency
actions, and dexterity will provide a transition to newer schemes
which will evolve more slowly. Another advantage of general purpose
techniques is the ease with which the size of the robot can be scaled
up or down. There appears to be no difficulty in making very large or
very small assembly stations which use essentially the same programs.
Versatile assembly devices have an efficiency advantage in medium
scale and job shop production; their utilization efficiency can be
higher because versatility allows a variety of operations to be
scheduled at one station. The availability of computers for
production scheduling will allow the flexibility to be used to
advantage. Robots would also have longer life over which to amortize
costs, since they could function with greatly changed production
operations, and evolve with new hardware and software modules. They
would have higher reliability, since they would have monitoring,
contingency action, and error recovery. They would be self-aligning,
self-calibrating, and self-testing.
With simple devices, inspection and assembly operations must be
broken down into small operations with transfer operations between.
This introduces extra transfer devices with orientation-preserving or
re-orienting steps, and overlapping operations which can be combined
in an advanced device. The extra cost of jigs and fixtures, and of
stopping assembly lines adds to the cost of special purpose
automation. Robots can cut down on real estate, since several
operations can be done at one station.
Because of the adaptibility of work stations, production runs can be
smaller, with less capital tied up in inventory and unused machines.
There can be better response to the needs of high technology industry
which often requires small quantities. There has been difficulty in
meeting the tight tolerances necessary for operation of
non-intelligent devices. It is hard to estimate the value of systems
which work within current industrial tolerance, rather than requiring
radical improvements in engineering practice, but that continuity
does seem important.
Serious issues arise in the development of robots with simple sensory
systems. Some evaluation issues should be settled by demonstration,
since much sharper analyses can be made this way.
There is already a significant degree of industrial involvement in
computer-aided assembly. GM Research Lab has demonstrated a wheel
mounting operation using visual alignment. IBM has a small robot
project. Hitachi has demonstrated a robot with touch and a system
with visual identification of objects on a conveyor belt, with visual
control of manipulation. Can industry be expected to adequately
support this development? Development of primitive robots is an
evolutionary process, not a single product development. The area is
too big; the payoffs are huge and the investment level is large.
Also, traditionally, industry has been dependent on suppliers. But
with the exception of IBM, the suppliers are smaller and less able to
risk the development. Management techniques require inordinate
return on capital. An alternative support could come again from the
military. The ARPA study and the history of numerical controlled
machine tools are natural consequences of the vast military
procurement in technologically advanced products. In the case of N/C,
funds could be diverted from an outmoded technology to development of
devices which were essential to production plans for the future. It
is high technology products which are most competitive
internationally in the civilian economy. The record of government
investment in numerical control, solid state electronics, computers,
and electro-optical devices has been very productive. However, in
large scale development of civilian products and international
markets, foreign governments have taken the lead. The development of
general purpose assembly devices will benefit manufacturers in a
broad spectrum of industry, particularly medium scale manufacturing.
It is an appropriate area for governmental action to help meet a
national need.
Given sensor-equipped robots at little extra cost over current
senseless robots, they will have visual ability to sense color,
strong contrasts in brightness, recognize known objects from two
dimensional outlines (top view of flat object) or three dimensionally
using depth, to a certain extent they will be able to recognize three
dimensional objects using monocular recognition. They will be able
to visually align mating parts in assembly operations, increase
reliability by self- calibration and flaw and disaster detection, and
permit large tolerances in parts transfer, even permitting parts to
be taken from a bin. Several of these abilities are not yet
available in the laboratory, but with known conditions and objects,
and some engineering (e.g. color coding) are achievable within the
time span of the proposed project.
The major determinant of the economic impact of assembly robots will
be the extent of their capabilities. Assessing and extending these
capabilities is the principal goal of the proposed project.
FIGURE 1: PUMP ASSEMBLY LAYOUT
A. ARM
B. GASKET
D. PALLET
E. POWER SCREWDRIVER
F. TOOL STAND
G. HOLE IN TOOL STAND FOR PIN
H. SCREW DISPENSER
I. PUMP COVER
J. PUMP BASE
L. STOP TO LOCATE PUMP BASE PLATE
M. PLATE FOR PUMP BASE
II. RESEARCH PLAN
II.A THE ASSEMBLY SYSTEM
An assembly system is not just a programmable assembly device,
important as that is at this stage of evolution. From an overall
standpoint, the biggest problems in automated assembly are system
problems. We suggest two main facilities of an assembly system that
relate to the total factory automation system: a) design of products
for automated assembly; as described above, many assembly problems
can be simplified enormously by design especially for the abilities
of automated assembly systems. b) programming of assembly devices for
new tasks.
Our experiments in assembly lead us to suggest a form for a general
assembly system, a mini and maxi system. The mini system is a small
configuration minicomputer-based assembly station (possibly stand
alone) tailored to a task by a choice of arm and vision modules, both
hardware and software. The maxi system is to reside in a factory
central computer which handles scheduling of resources, design,
communication, routing and tracking of parts, etc. The maxi system
interfaces the assembly station with the central computer and
provides the capability for convenient design for automated assembly
and convenient reprogramming of the assembly station for new tasks.
The maxi system takes advantage of design information in the central
system and large file system, graphics, and problem-solving
languages. Its use is shared among many machines since reprogramming
is done at intervals, only when tasks are changed. The mini system
is low cost, yet its behavior can be pre-planned and optimized by the
maxi system, utilizing intelligent and efficient planning.
We suggest that programming robots requires an ease of communication
with the system that depends upon advances in strategy modules for
assembly, to raise the level of the language used. We also suggest
that many system design problems will be simplified by providing
facilities for product design for assembly. The designers can
interact with the system which programs the assembly device, and with
the aid of simulation modules, find out which operations are critical
or impossible.
The essential parts of the system are: a) representation
(descriptors) of shape and assembly operations. b) Interactive
systems including graphics. c) Strategies which allow generality. d)
Description of the assembly process in terms of program elements. e)
Arm and vision software and hardware modules.
II.B WHAT HAS BEEN DONE
In brief, we have demonstrated in an assembly task some simple visual
operations combined with a general system for programming a
manipulator controlled by force and touch sensing. The manipulator
was designed and built according to software requirements based on
our earlier experience. We have made significant progress in
defining descriptors for assembly.
Our previous work on representation of shape of complex objects for
visual description and recognition provides a strong background for
study of descriptors of objects and operations for assembly. The
representation is based on a part/whole segmentation of objects into
parts. The choice of parts is crucial; in our representation, the
parts are volume parts defined by cross sections along a space curve
axis. That representation of primitive parts is based on a formalism
"generalized translational invariance". Descriptions within that
representation can be constructed symbolically, or with some
modification to the geometric editor GEOMED [Baumgart 1972a]. These
descriptions can now be displayed by the display program OCCULT
[Baumgart 1972b]. The ease with which we can generate models of
objects and the ability to obtain symbolic information with display
techniques are strong points of our graphics techniques.
Some of the preliminary efforts have been made in automating strategy
generation. The language design efforts have produced new language
features, such as those in SAIL [Feldman 1972]. Considerable
discussion and experience have been gained by a course and research
efforts using those language features.
Our first major task was assembly of an automobile water pump. A
film which shows the process in detail is available for distribution.
We describe the task in some detail to show the level of programming
the system and the level of ability of the programmable device.
Figure 1 shows a drawing of the assembly layout with pump base,
cover, gasket and screws. We have chosen a plausible industrial
environment with tools in fixed places, screws in a feeder, and pump
body and cover on a pallet. The pump body is mounted on a plate,
which can be placed anywhere on the table, since it is located by
vision. Then it is moved to a standard position determined by stops.
Pins are inserted into screw holes in the pump body to guide
alignment of gasket and cover; if necessary the hand searches to seat
the pins. The gasket is placed over the guide pins and visually
inspected for correct placement. The cover is expected to be within
about a quarter of an inch of its fixed place on the pallet. After
locating the cover by touch, the hand places the cover over the guide
pins. The hand then picks up a hex head power screwdriver, picks up
a screw from the feeder and inserts the screw; if necessary, the hand
searches to seat the screw. A second screw is inserted. The hand
then removes the two pins and inserts four more screws. Finally, it
tests that the pump impeller turns freely.
Three forms of feedback are used, visual, tactile, and force. The
visual feedback is provided by strictly special purpose programs
which have no general interest. In a later section we show how we
plan to generalize visual feedback capabilities and include them in a
system like that used to program the rest of the task. The
manipulation parts of the assembly were programmed in the Hand
Language, HAL. The actual program follows:
BEGIN PUMP
ALIGN ;COMMENT ALIGN PUMP BASE AT STOPS
PIN P1 H1 ;COMMENT PUT PIN P1 AT HOLE H1
PIN P2 H2 ;COMMENT PUT PIN P2 AT HOLE H2
GASKET
TOP
SCREW1 ;COMMENT PUT IN FIRST TWO SCREWS
UNPIN H1 P1 H1A ;COMMENT REMOVE PIN P1 FROM HOLE H1
UNPIN H2 P2 H2A ;COMMENT REMOVE PIN P2 FROM HOLE H2
SCREW2 ;COMMENT INSERT LAST FOUR SCREWS
TEST
END
Each of the commands is a macro, an example of which is defined here.
What follows is a Hand Language program to insert a pin in a hole.
The position of the hand to pick-up the pin is refered to as P. This
position is defined by moving the hand to the location of the pin and
typing "HERE P"; the program then reads the current position of the
hand and stores it in P ("learning by doing"). Similarly the
position just above the hole where the insertion is to begin is
defined by "HERE T". This is done with the pin in the hand.
DEFINE PIN
MOVE P ;GO TO THE PIN
CLOSE 0.1 ;PICK UP PIN
MOVE T ;GO TO THE HOLE
SEARCH .07 ;SET SEARCH INCREMENT
L1: MOVE T ;GO TO THE HOLE
STOP [0 0 -50] ;SET UP STOP ON FORCE CONDITION
CHANGE [0 0 -1] 0.6 ;TRY TO GO DOWN WITHOUT RESISTANCE
SKIPE 23 ;SKIP IF NO FORCE ENCOUNTERED
AOJ L1 ;ADD SEARCH INCREMENT AND JUMP
STOP [0 0 -60] ;SET UP STOP ON FORCE CONDITION
CHANGE [0 0 -1] 0.7 ;SHOULD MEET SOME RESISTANCE
SKIPN 23 ;SKIP IF FORCE ENCOUNTERED
AOJ L1 ;ADD SEARCH INCREMENT AND JUMP
SAVE H ;REMEMBER WHERE HOLE ACTUALLY IS
OPEN .5 ;RELEASE PIN
CLOSE -.1 ;CHECK THAT IT IS STILL THERE
OPEN 1 ;RELEASE AGAIN
The pin should go into the hole without resistance, then encounter
resistance. If it does not encounter resistance at the proper
height, then the pin is off the pump body and the search resumes.
The most obvious form of programming robots is that in use with
current industrial robots, "learning by doing". The robot is led to
end points of a trajectory or along a path by an operator. As we note
from the example shown, positions for the pump assembly were
specified in that way. This facility is embedded in our general
interactive language system, which is not special to the pump task.
"Learning by doing" techniques are limited to tasks with positional
control only. Where force control is necessary, the control
program must know in what way to control forces, i.e. what forces to
exert or null along which direction vector. For example, in
inserting a rod into a hole, transverse forces should be nulled. Such
tasks require specifying a trajectory with additional symbolic force
control information. Since arm control will be primarily based on
minicomputers in new designs, a a small interactive system such as we
use is quite natural. For any task involving a sequence of such
operations, the interactive system allows the operator to choose from
a menu of operations and proceed step by step to assemble a sequence
of these operations which have been verified. Checking the success
of these operations could use simulation if necessary to avoid tying
up a production machine. Useful modules like inserting a screw would
be saved as macros so that subsequent programming sessions would be
much simplified, dealing with combinations of such high level
modules. The macros can have arguments, so that one basic operation
can be used in a variety of situations, and further, our software
allows modifying trajectories so that a planned trajectory allows
considerable variation in execution, without replanning. The ability
to transform positions and orientations means that an assembly
programmed for one position and orientation can be performed anywhere
in the working space without reprogramming. Ail these facilities
exist in our interactive system for the hand.
What elements of the system are general or generalizable? We see the
pin insert and screw insert as generally useful modules which depend
on the search module which depends on lateral and vertical force
feedback. The use of tools such as the power screwdriver, the
screwfeeder, and guide pins are generally useful. In general, jigs
are special purpose but often inexpensive. The operation of location
and orientation by touch depended upon a general system facility, but
required a special program for the special shape. The hand language
and interactive system are general enough for a single arm.
There appear to be several evolutionary stages at which the
production engineer can be aided greatly by assembly system software.
The first is through a general programming language. The second is
through the aid of model-based facilities and simple strategies which
raise the level of language for the user. At this level, the system
is probably acceptable to program, and allows use of the assembly
system to simulate the assembly process as an aid in designing for
automated assembly. At a later stage, much of the detail can be
eliminated by strategy programs which accept commands at a high
level, at least as high a level as an instruction manual.
II.C WHAT WE PROPOSE TO DO
We discuss the next steps by considering the extensions to the system
which would allow us to accomplish a task similar to pump assembly,
and a much more difficult assembly task, assembly of a two stroke
gasoline engine. We estimate that mechanical assembly of an
alternator from palletized form would require only about ten hours
reprogramming after building a jig to hold the bottom of the
alternator. If parts were not palletized and vision were required,
the special vision program would require several weeks programming.
This emphasizes the importance of vision language system development.
Assembly of a two cycle gasoline engine is different and much more
difficult. What new operations would be required? A vise, perhaps a
universal vise, a sleeve to insert roller bearings and seals,
manipulating a roller to apply liquid gasket material, universal
joint and long shaft hex head drivers, aligning and inserting shaft
keys, generalizing orientation and location by touch, improving touch
and force sensing (particularly peripheral touch) and use of two
hands. Programming a new operation of the complexity of inserting a
screw and torquing it down requires of the order of eight hours
programming.
How much is special purpose and how much general? Most of the
additions we have just mentioned are general. Certain jigs and
sleeves will be necessary, just as they are for assembly by humans.
Most of the operations are of the form "put x into y"; they are
special purpose to the extent that special ways of gripping are
necessary to avoid obstacles. Initially, human interaction must
specify those special grasping procedures, but with a second stage of
development of obstacle avoidance modules and strategy modules, much
of the difficulty can be eliminated.
These observations lead us to propose steps which will lead to a
better understanding of the design of assembly systems. One effort
includes:
Top-Down system design
GOAL-DIRECTED, MODEL-DRIVEN SYSTEM
characterize objects and assembly operations by descriptors
(representation)
problem definition of a class of assembly systems
strategies and interactive system for automating programming
of assembly operations in terms of our primitives
APT-like language design
gedanken assembly experiments and generalize requirements
We have seen that a general interactive language system adds
considerable generality to a programmable assembly system. To
greatly extend this generality and thus the ease of programming, the
system should be model- driven. We already have one example of such
a visual system in our laboratory, which has been used for two visual
domains, outdoor roadside scenes and cardiac angiograms [Yakimovsky].
Only a change of models, which were experimentally obtained by a
learning system, was necessary to go to the second domain. There is
work underway which combines goal-direction with a model-based
system.
For this aim of generality, the investigation of representation and
implementation of a wide range of examples of object descriptions
within the representation should be carried out. These will be
necessary for a system which produces strategies for programming
operations in terms of plausible primitives, from input at the level
of an instruction manual. Human interaction will be necessary for
special problems.
It is clear that we must cycle a few times through top-down system
design and evolutionary system building. That experience is the
basis on which to design an APT-like language for assembly. Of
course, there should be computer science inputs into the language
design. Even good design from a system software standpoint could
avoid some of the mistakes of APT.
EVOLUTION OF SYSTEM
select real experiments from gedanken experiments, based on
generality of modules, economic significance,
feasibility
design and implement necessary modules
incorporate and test system.
The examples above illustrate these procedures. In many cases, we
cannot proceed far without design and implementation of tools and
modules. For example, an important need is for hands with more than
two fingers and greater dexterity than now possible. We don't really
know what will be possible, how to characterize the behavior of such
hands, how to program them. Attempts to characterize them may be
optimistic and unrealistic. The situation may be different from that
of machining parts, for which we expect current machines to be used
for a long time to come, and for which much experience is available
with machines and techniques. We must share efforts between a
"top-down" design and an evolutionary effort producing an actual
system to keep system design honest.
Our previous discussion provided motivation for important incremental
extensions of our existing system, to two arms in coordination,
obstacle avoidance, tools, improved touch and force sensing, and more
general location by touch. Current work is involved with extending
the hand progrmamming language to include more than one arm. Problems
of synchronization and coordinated motion will be provided for. A
module for obstacle avoidance in trajectory planning will be
included. Later, the hand language will probably be the output
language for a higher level language for manipulation,in which tasks
such as inserting, testing, and mating will be expressible in a
simple manner. We will generalize the operation of locating and
orienting by touch, and improve the touch and force sensors.
To simplify and systematize the programming of visual feedback
operations, an interactive vision language system will be designed
and implemented. We plan that it resemble the hand language,
embedding the useful set of visual operators in a convenient
interactive system. It will be based on current design ideas,
elaborated by a series of experiments. We intend eventually to
automate programming of visual feedback, by using prediction of the
projected appearance of edges and surfaces of objects from models,
using our representation and symbolic display facilities. We will
use these predicted curves as guides for a set of operators intended
for finding a predicted curve in a small window of an image. This is
one step toward broad generality, moving to a model-based system.
We will transfer our assembly system to include the mini system in a
minicomputer which we are acquiring. There is the beginning of
user's group sharing the same minicomputer (GM Research Lab, 3M
Research Lab, JPL, and MIT Artificial Intelligence Lab). We can show
the relevance of our research to low cost systems for industrial
applications, by direct transfer of our research results to
industrial observers and laboratories. Our hand language, arm
control and arm trajectory planning software are suitable for a
variety of arms, including the Unimate.
This system can be thought of as a model for a programmable assembly
device. Each assembly station will include a minicomputer and
manipulators with cameras combined as modules in a system to suit a
given task. The software will be modular to accomodate varying
hardware configurations. The minicomputer performs servo control and
contingency actions for the arm, and elementary visual operations.
Touch and force sensing operations will be included.
III. MANAGEMENT PLAN
The proposed research will be carried out by academic faculty,
research staff and students of the Artificial Intelligence Laboratory
Stanford University. The responsibility for planning, coordinating,
and supervising the proposed work will be with:
Prof Jerome Feldman
Artificial Intelligence Laboratory
Computer Science Department
Stanford University
Stanford, California, 94305
He will devote 1/6th of his time to this project.
The organization conducting the proposed research is:
Artificial Intelligence Laboratory
Computer Science Department
Stanford University
Stanford, California, 94305
Stanford University is a non-profit educational institution
incorporated under the laws of the state of California.
The legal-fiscal agent is:
Lester D. Earnest
Artificial Intelligence Laboratory
Computer Science Department
Stanford University
Stanford, California, 94305
III.A SCHEDULE
The following schedule shows the approximate distribution of effort
among the principal tasks:
Top-Down System Study 50% of effort
Strategies
Language Design
Descriptors
Gedanken Experiments
Needs
Evolutionary System Building 50% of effort
Visual feedback
Arm Hardware
Arm Software
Task Execution
Mini system
The personnel involved are:
Prof J.A.Feldman will assume overall coordination, and guide design
of the language for assembly.
Dr. T.O.Binford will guide development of the vision language and
visual feedback for assembly, extend work in representation, and
direct the top-down system study.
Dr. R.Paul will implement software for the second arm and
coordination of two arms, will direct efforts in collision avoidance,
and extend the hand language.
V.Scheinman will design and build tools, special hands and arms, and
contribute to hardware study for the top-down study.
Prof. C.C.Green will guide efforts in strategies for the top-down
study. (Prof Green will not be supported by the proposed contract.)
K.K.Pingle will provide system support and some implementation of the
vision language primitives.
R.C.Bolles will perform an advanced assembly task as the first in a
series of planned experiments.
R.H.Taylor will formalize a class of assembly tasks in terms of
descriptors and obtain strategies in terms of assembly primitives
from input at approximately the level of an instruction manual.
A third student will work on descriptors and the vision language.
III.B MILESTONES
During the first nine months we will improve the problem definition
and present a coherent structure for the following phases of the
project.
After the first two thirds of the contract, we will understand and
have solved the major problems in two hand coordination, and have
operating software.
A class of easily-defined objects will be described in terms of our
descriptors, and will be encoded for later strategy work.
At the end of the period, we will have a formalization and major
progress on a strategy program which will go from language at the
level of an instruction manual to a sequence of hypothesized
primitive actions which seems adequate and plausible for execution of
the specified task.
Significant sub-assemblies of a two-stroke gasoline engine will be
reliably operating, using primitive versions of the hand and eye
languages.
A primitive vision language will be operative, including correlation
operators, hole detectors, and edge and curve matching.
A major part of the conversion to the mini system will be performed.
IV RELATED PROGRAMS AT THIS LABORATORY
IV.A PAST RESEARCH IN VISION
Although we propose an entirely new direction for the laboratory,
past and continuing research form an important background. Research
in computer vision has progressed so that most of our effort is
directed toward vision in real world scenes. We outline past
research, including analysis of some outdoor scenes, interpretation
of scenes with curved objects, work on visual feedback, development
of useful modules, and a study of sensor systems.
In understanding outdoor scenes or in performing visual feedback
operations for industrial environments, it is important to
incorporate semantics for the individual problem domain and to
provide simple ways of reprogramming for different problem domains. A
working version of such a system [Yakimovsky] has been successful in
segmenting and labelling regions in two different problem domains,
outdoor scenes and cardiac angiograms. It was not difficult to go
from outdoor scenes to angiograms; some changes were made in the
region descriptors, but the major changes were made in the world
model by an interactive learning process from sample pictures. The
system sequentially merges regions with the weakest boundary first.
In initial steps it proceeds without semantics. Then, with an
intermediate number of regions, it estimates the best interpretation
using a world model of two-dimensional image properties. A linear
evaluation of interpretation includes not only region properties in
isolation (i.e. sky is blue) but the properties of its boundary with
other regions (sky is brighter than trees). This is a "first-order
semantics" of relations. From analysis of some examples, the system
performs much better with semantics than without.
Understanding outdoor scenes is a more complex task than many
industrial visual operations, but it has utility for planetary
exploration; it is also a useful exercise for developing color,
texture, and stereo operations. Bajcsy analyzed outdoor scenes with a
combination of color and texture region analysis [Bajcsy 1972]; depth
cues were taken from the gradient of texture. An attempt was made to
obtain consistent interpretations by making three dimensional
interpretations, and confirming boundaries by independent properties.
Natural objects and natural scenes have been used for our work with
motion parallax [Nevatia in draft] and stereo vision
[Quam,Hannah].These are complementary techniques of depth perception.
Motion parallax can be used for planning of navigation and
characterizing large scale terrain features, since it provides large
baselines.
Our past work on visual feedback [Gill 1972] in stacking blocks and
inserting objects in holes is directly relevant to vision for
assembly. The issues involved are: coordinating hand and eye,
utilizing incremental accuracy of vision and manipulation, accurate
prediction to limit search, and limiting accumulation of errors.
Visual guidance in picking up objects and stacking or inserting
objects overcomes errors in the visual and arm coordinate systems,
and errors in correspondence between the two coordinate systems. This
was done under realistic conditions, with the hand still holding the
object and in view; the system predicts the appearance of edges and
corners within a small window around the edges to be aligned, and
uses a corner/edge finder to determine alignment error. The arm makes
accurate incremental error correction. If predictions can be made
accurately, the search window can be small, which increases the speed
of response and decreases possibility of confusion with other areas
of the scene. A major limitation to accuracy of prediction (and to
long-term reliability in the industrial environment) is drift of
calibration. That work incorporates calibration updating by
self-calibration to coordinate hand and eye coordinate systems and
provide for reliability, as well as to limit search by accurately
predicting expected features. Visual feedback with stereo vision is
much more powerful than without; we have demonstrated stereo visual
feedback in a program [Binford unpublished]. A corner finder for
visual feedback with polyhedra has been developed which is able to
function well under realistic conditions of low contrast, with more
complex backgrounds [Perkins].
Another aspect of vision with direct application to industrial
environments is explored in our work with curved objects. Current
programs use depth information to make useful descriptions of objects
like a doll, a toy horse, a snake, or a glove [Agin]. Programs can
segment these objects into parts like legs, arms or fingers, and link
these parts together to form descriptions of entire objects
[Nevatia]. These operations are precursors to a recognition process.
The work depends on a new representation of shape [Binford 1971] in
terms of volume components and a part/whole segmentation. Primitive
parts are described by an axis which is a space curve, and normal
cross sections along the axis. The representation is based on a
formalism, "generalized translational invariance". There are
significant theoretical reasons why this representation leads to a
natural choice of parts by simple procedures, in distinction to other
suggested representations. We developed a laser ranging device
[Binford 1970,Agin] which was operational two years ago. It is
particularly interesting that the device adds a cost of only about
$400 to the cost of a visual system.
We have a set of visual modules to be used in a goal-directed way.
These modules answer questions which arise in practical visual
feedback tasks, and in analysis of outdoor scenes. Are there color
edges in a selected window? Are there edges in brightness? What is
the color of the region in the window? Several preliminary color
region analysis programs have been developed [Binford, unpublished,
Bajcsy 1972]. An edge operator [Hueckel 1969] and a region-based
corner/edge finder [Gill 1972] both operate on small windows of the
scene, as directed by a higher level. An edge verifier is used to
confirm or reject edges predicted from context [Tenenbaum 1970].
Color identification has long been demonstrated. Recently, a program
has been implemented [Thomas unpublished] based on Land's Retinex
model of color constancy in vision, which defines a color triangle
from ordering of brightnesses. The center of the triangle is
designated as white. The program shows a color constancy similar to
humans; table top scenes are assigned colors reasonably well under a
variety of lighting with different spectra: sunlight, flourescent,
and incandescent.
A study of visual and depth ranging sensors has been carried out
which provides a background for choosing sensors and interface, and
making cost estimates [Binford 1972, Earnest 1967]. We have
maintained a calibrated hand and vision system for several years
[Sobel 1970], and have developed self-calibration techniques with one
and two eyes [Yakimovsky unpublished, Hannah 1972, Gill 1972].
A system for understanding scenes of blocks from single views has
been implemented [Falk 1970]; it segments objects, matches against
internal models, predicts appearance from its assumptions, and
searches for verification and contradiction based on the predicted
image. Support and spatial relations are also obtained. There has
been past work on programs to obtain line drawings as input to
recognition systems. An earlier system was based on limited context
of expecting continuation of edges at vertices and predicting missing
edges in certain expected configurations [Grape 1970]. A more recent
approach has been to use models within the process of generating line
drawings, so that the most efficient next guess can be made and
context used intrinsically in the formation of line drawings [Grape
1973].
IV.B CONTINUING VISION RESEARCH
While continuing research activities are not to be supported by the
proposed project, in all likelihood, the research will suggest useful
programs for the assembly system, particularly work in recognition of
complex objects using depth data. The computer now has symbolic
internal models of objects. We are now in a position to recognize
objects such as a glove, a hammer, etc. We will immediately deal
with matching descriptions for recognition. The important extensions
we wish to deal with are occlusion and access to a large visual
memory. These will involve matching of partial descriptions and
encoding of partial descriptions as indices to a small class of
similar shapes, which can then be compared in more detail. We are
working toward making the hardware stable enough that it can maintain
calibration, and software streamlined so that the depth measurement
system can be integrated with other hand/eye facilities. We intend
to apply large parts of our programs for description and recognition
of curved objects using only monocular intensity information. This is
a much harder problem, but within our representation, shares much of
the same code, and relies on the same conceptual basis of local
three-dimensional models.
There is a growing effort on navigation of vehicles in outdoor
scenes. While this is not directly relevant to the industrial
environment, it does reflect the growing involvement in complex
visual tasks, and will lead to further development of an integrated
visual system.
IV.C MANIPULATOR DEVELOPMENT
Manipulator research began at the Stanford Artificial Intelligence
Laboratory in 1965 with the acquisition of a Rancho Orthotic Arm.
This arm was interfaced in such a way that the computer could read
the joint positions by means of potentiometers and drive the arm by
means of a voltage, pulse rate modulated signal for each joint. Two
solution programs were written: one to give the rectangular
coordinate position of the hand knowing the six joint angles, and one
to give the joint angles knowing the desired x,y,z position and
orientation of the hand. These programs were then combined with a
servo program to move the hand.
The servo program represents two key concepts of all subsequent
manipulator research at this laboratory: 1) To provide a digital
computer with all sensory inputs, initially joint positions, but
later velocity, force and touch measurements. 2) The utilization of a
programable digital computer to take the sensory inputs and to
provide basic control output to the manipulator, in our case joint
torque. Initially the sensory input of joint angles was to provide a
position servo with a sampling rate of 60 cycles per second. The arm
control has been changed many times since, a simple task in the case
of a program, but difficult in the case of a hard wire servo system.
This system was used by Wichman, Pingle and Singer in 1968 to provide
a program which could pick-up, sort and stack blocks, and in a first
attempt at visual feedback.
At this time Pieper considered the kinematics of manipulators and
developed a classification scheme for all possible arms and their
analytic solvability. He concluded his work by implementing a
trajectory based collision avoiding program. Although slow, this work
demonstrated the need for trajectory control of the arm.
Kahn followed Pieper with a study of the dynamics of manipulators and
developed a "bang-bang" servo for a new hydraulic arm. This arm,
still at the laboratory, was developed as a high performance
manipulator.
By 1970 a general purpose program capable of manipulating plane faced
bodies was implemented. This program was used in the initial version
of the "Instant Insanity" program where all possible reorientations
of the four colored cubes were required in the puzzle solution.
Our present arm, the result of a continuing design project, replaced
the Rancho Arm in 1970. This arm, like its predecessor is controlled
by a digital servo program running under the time sharing system. The
new arm was used in the "Instant Insanity" demonstration in 1971.
As this arm is driven by electric motors through high efficiency gear
reductions it is possible to predict the torque exerted at each joint
in terms of drive level. Based on kinematic analysis it is possible
to calculate the joint torques necessary to produce a given force at
the hand. Further, for the hand to exhibit degrees of freedom of
motion, various joints are selected to be "free," that is, to have no
position feedback applied to them. By exerting a force at the hand
and providing that certain degrees of freedom exist it is possible to
perform a much wider class of tasks than simple positional control,
for instance screwing in screws, mating parts, inserting tools and
turning cranks.
Positional accuracy at the hand is typically 0.1 inches with
repeatability of 0.02 inches. Force control at the hand is within 10
ounces. The data rate to run the arm from an assembled arm program is
of the order of 100 bits/second.
Touch sensors on the fingers allow the hand to sense objects without
disturbing them. When activated the arm will stop when any of the
sensors touch. Branching arm instructions can test the state of the
sensors and modify the execution of an arm program. When screwing a
nut onto a bolt, for instance, the hand is first centered over the
bolt by touching it.
A second arm of the same type, with only minor modifications, is
currently being interfaced to the computer. Both arms will share a
common work space and will be able to be used individually, or
together for two-handed tasks. This should increase the scope of our
manipulation work by some orders of magnitude. It may be possible for
many assembly workers to work with their eyes closed but it would not
be possible, in most cases, to work with only one arm.
Control of the arms is achieved by a series of high level
instructions; these instructions may be executed directly, or may be
assembled together into an arm program. Arm programs may then be
executed, as desired, by the control program. The control
instructions may be accessed directly by other programs, using the
message procedure mechanism, or in the form of a symbolic source
file.
IV.D SYSTEM REQUIREMENTS
A key element in the development of any large programming effort is
the software support. There has been continual effort along these
lines at the Stanford Artificial Intelligence Laboratory and many
useful ideas have been developed. We propose to extend the research
along two separate lines: support for research and system software
for operating automated systems. We will briefly describe our past
efforts in this area and what we propose to undertake.
The Stanford Artificial Intelligence Laboratory has pioneered in
several areas of system programming, including timesharing systems,
interactive graphics, and programming languages.
A great deal of additional effort has gone into direct support of
hand-eye research. Very early, we established a set of standard
formats and library routines [Pingle 72a]. A programming language,
SAIL[Swinehart], was implemented in 1968. The language incorporates
special features for large real-world-oriented problem solving
systems and is currently in use at a number of Artificial
Intelligence and other laboratories.
As we began to assemble larger and more sophisticated collections of
programs, we felt a need for flexible mechanisms for cooperating
sequential processes. The resulting monitor and language features
[Feldman & Sproull 1971] have worked well for us and have been
influential on other projects. Recently, we have added an
additional set of features to SAIL to facilitate non-deterministic
and event-directed programming[Feldman 1972]. The early reactions
to this work, both within the laboratory and without have been very
encouraging.
We expect relatively little work to be needed on further systems
support for basic research. There will undoubtedly be efforts to
take advantage of new hardware and of new programming ideas that may
develop in the course of this program. Almost all of this work
should be covered by other funding.
V. RELATED PROGRAMS IN OTHER ORGANIZATIONS
Major programs in programmable assembly devices with sensing are:
1. Hitachi, Ltd, Tokyo, Japan
2. Electrotechnical Laboratory, Tokyo,Japan
3. Draper Laboratory, MIT, under James Nevins
4. SRI, Menlo Park, Calif, under Dr. Charles Rosen
5. IBM, Yorktown Heights, NY,under Dr. Peter Will
6. GM Research Lab
7. MIT Artificial Intelligence Laboratory,
under Profs B.K.P.Horn and P.H.Winston
8. University of Edinburgh, Edinburgh, Scotland with
University of Nottingham, under Prof Donald Michie
9. University of Rochester, Rochester New York, with Gleason
Works, under Prof Herbert Voelker.
Hitachi,Ltd has performed important basic and applied research in robotics
and computer perception including:
a system which reads blueprints and constructs a structure
from isolated parts, using vision
a system which packs blocks into a tray, using touch sensing
a system which recognizes objects on a conveyor from top view
and which acts on them accordingly
a system which locates faults in printed circuit boards, and
which appears to be commercially attractive.
Their current effort is in applied research for current problems, including
corporate manufacturing problems.
Electrotechnical Laboratory, Tokyo, is a government laboratory performing
basic research in robotics and computer perception, including:
manipulator hardware and software development
visual feedback for insertion
recognition of polyhedra using depth data.
Draper Lab, MIT, has performed basic research in teleoperator control,
and now has a collaborative effort with General Electric and Unimation
to develop an adaptive, programmable modular assembly device with force
sensing.
SRI has performed basic research in teleoperator control and robotics,
including:
control of a mobile robot, emphasizing problem-solving
teleoperator control including touch and computer interfacing
They now have a multi-client research contract for applied research in
visual inspection from two-dimensional views (top view), and for
visually controlled manipulation, using a Unimate programmable manipulator.
IBM has performed basic research in vision, and are now engaged in
research in manipulation with sensory input.
GM has performed basic and applied research in vision, as demonstrated
by a film showing the mounting of an automobile wheel, using visual
We have close contact with all of these laboratories.
Artificial Intelligence Laboratory, MIT, has done basic and theorectical
work in perception and manipulation including:
perception of line drawings from symbolic input
edge and region systems to obtain line drawings
duplication of towers of blocks from single views.
This fundamental work continues, supplemented by a research project in
mini-robot systems.
The University of Edinburgh has performed basic research in vision and
manipulation, demonstrated by a film showing assembly of a toy car using
simple vision to identify parts.
The University of Rochester is engaged in basic research applied
to discrete parts manufacture, addressing the problems of descriptors,
needs, and formalizing the structure of systems for discrete parts
manufacture.
It is of special interest to indicate the relation of our work to
laboratories which have industrial clients, such as GM Research Lab,
SRI and Draper Lab. Their contacts are a valuable source of
information about industrial problems at the level of implementation
with currently available techniques. Because of the ovelap of
interests and techniques, a two way exchange is possible, since our
research is considerably advanced beyond those projects and will map
out solutions to problems to be faced in later phases of their
projects.
VI. UTILIZATION PLAN-DISSEMINATION OF RESULTS
There are several other efforts, at varying levels of sophistication,
in the area of computer controlled advanced automation. The Stanford
Artificial Intelligence Laboratory is the oldest and largest of these
and has provided the cutting edge of much of the development. We
intend to continue this role and to lead in the development of a
practical general purpose assembly system and the associated
software.
All of the discoveries made under this grant will, of course, be made
public. We view our role in information transfer in a much broader
way. Having no desire to sell products and services and no clients
with interests to protect, a university laboratory should be
primarily a place for acquiring and spreading knowledge.
The Stanford Artificial Intelligence Laboratory has always considered
information transfer to be one of its primary goals. In addition, to
our published papers and reports, we have made a number of film
reports which have been extensively circulated. There have been
thousands of visitors, ranging from elementary school classes to
major scientific, governmental and industrial missions from many
countries. We have also encouraged extended visits by workers from
other laboratories and from industry. Members of the laboratory staff
have played a major role in the organization of journals, conferences
and seminars in artificial intelligence. Many of our algorithms,
systems, and hardware designs have been adopted by other groups. Our
students are an important contribution to information transfer.
We have close contacts with SRI (several former students are employed
there) and with Draper Lab, MIT. These laboratories, with industrial
participants in their research programs, provide the right setting
for transfer to industry. We have close contact with the computer
science group at GM Research Labs.
A development of considerable interest is that five groups doing
similar research have chosen the PDP-11 as a research minicomputer.
We will have immediate relevance to members of this informal robot
user's group. Our programs will be usable by members and any others
who acquire the PDP-11. It would be advantageous for future
purchases of minicomputers for automation research, that they be
compatible with this group. Our efforts are exportable in a further
sense; the arm servo and planning programs are applicable to a wide
range of arms, to the Unimate in particular.
The success of the program proposed here depends heavily on
interaction with industry. We will need additional information on
the opportunities for automation and will try to involve manfacturers
in the development of the pilot assembly station. We have good
working relationships with a number of firms and we expect that a
concrete project will help forge the bonds necessary for the transfer
of advanced automation technology from the laboratory. During the
initial proposed grant period, we intend to negotiate a cooperative
relation with one or more firms.
One promising approach to technology transfer is a staff associates
program. This involves residence of about a year's duration at the
laboratory by industrial scientists. Such a program was used
successfully at the MIT, Electronics Research Laboratory in the area
of programming systems for numerically controlled tools and design
automation. We have initiated a similar program in the laboratory
(cf Appendix A) and are pursuing additional forms of cooperation.
Appendix A
The Stanford A.I. Lab has been engaged in research on
advanced automation technology. Our work has progressed to the stage
where we are seriously engaged in developing and disseminating
practical applications. To do this effectively, we will need the
help of industrial organizations with computer sophistication and an
understanding of practical problems. One form of cooperation which
we are engaged in is a staff associates program. A staff associate
is a member of another organization in residence at the A.I. Lab. We
have had a number of staff associates over the years, mostly from
foreign countries. The enclosed letter from Japan is typical of the
inquiries we receive. The purpose of the new program is to invite a
few domestic inductrial organizations to submit candidates for the
staff associates positions.
We expect to be able to have three to five staff associates
in the laboratory at a time. The associate would remain for about a
year and would be paid by his sponsoring organization. He would be a
regular member of the laboratory staff, working on automation
problems.
There are, of course, academic programs at Stanford which
could also involve work at the Artificial Intelligence Lab. The
staff associate program differs in that the selection criteria are
different, the timing more flexible, and the expected time devoted to
course work much less. We believe that it is the most effective
mechanism for us to learn from and help industrial practitioners of
computer-aided advanced automation.
Jerome A. Feldman
APPENDIX B
TWO-CYCLE GASOLINE ENGINE ASSEMBLY
COMMENT PISTON-CRANK SUBASSEMBLY, RTV, BOLTING TWO HALVES;
COMPLETION OF THE PISTION-CRANK SUB ASSEMBLY;
Place the piston and crank shaft subassembly in the vise
(probably require higher sides on vise ... &/or fitted grips)
Put the sleeve on the clutch side of the shaft
Put the roller bearing on the clutch side
Put the metal-rubber seal on the clutch side
Take off the sleeve
Put on the large ball bearing on the flywheel side
(appears to be a heat fit ... would involve putting the
bearings on a hot plate and using `hot pads' to pick them up)
Put the metal-rubber seal on the flywheel side
Take the piston, crank, and bearings subassembly out of the vise
APPLYING RTV TO THE CASING
Place the cylinder head casting in the vise
(another fixture for holding the case firmly, without
damaging it ... or the paint job)
(at this point may want to use vision or touch to determine
the final orientation of the case)
Pick up the RTV roller
Roll the roller on the palette containing liquid RTV
(need a dispenser which provides just enough for one
application ... avoids the problem of deciding how much
you have on the roller)
Apply RTV to the joint of the casing
Put down the roller
JOINING THE TWO HALVES OF THE CASING
Pick up the piston-crank subassembly and dip the head of
the piston in an oil bath
Seat the piston-crank subassembly in the cylinder
(this requires some WOBBLING)
Place the bottom half of the casing on top of the cylinder
head half (which now contains the piston and crank)
(probably want to use 1 or 2 aligning pins)
Pick up the screw driver power unit
Attach the long-shafted driving parts
(this means a socket extension rod and a deep socket)
Screw in the four main case screws
(need two hands and a torque wrench to torque these screws)
Return the long-shafted driving parts
Attach the extension rod and deep socket for the small screws
Screw in the four small case screws
Return the rod and socket
Return the screwdriver power unit
COMMENT PUT ON THE CLUTCH, POINTS ASSEMBLY, AND FLYWHEEL;
PUTTING ON THE CLUTCH
Put on the inside shim for the clutch
(picking up one shim may be a trick)
Put on the roller bearings for the clutch
Put the clutch dish over the roller bearings
Put on the outside shim for the clutch
Put on the clutch subassembly
(interesting because it goes inside the dish ... put
it on the shaft and then push it on the rest of the way)
Put on the nut to secure the clutch (left-handed threads)
(again need long handled wrench to torgue this to the
required 300 inch/pounds ... also need to hold on to
the flywheel (when it gets on) because there is a
left-handed nut on one end and a right-handed nut on
other)
PUTTING ON THE POINTS SUBASSEMBLY
Insert the aligning pin for the points assembly
Put on the points assembly
Screw in the first points screw
Take out the aligning pin
Screw in the second points screw
Take out the feeler gauge tab (which cames as part of the
points subassembly ... not sure this is going to work)
(it is important to make sure the shaft is still
rotated to the proper place ... visual check for slot?)
Press on the cover to the points assembly
(good reason to have fixture holding case on four sides
because this will be a strong sideways force)
Screw in the final points assembly screw
Screw down the condenser
(this is tricky because the wire holding the condenser
has to be bent in just the right way (or use two hands)
... plus the screw has two washers (magnet hold them?))
PUTTING ON THE FLYWHEEL
Insert the key for the flywheel
(picking up the key is a problem (magnet which can be
turned on and off ... needlenose fingers ... )
Put on the flywheel
(visually determine the orientation of the flywheel)
(flywheel probably needs to be tapped on)
Put on the nut to secure the flywheel
.
.
.
VIII VITAE AND BIBLIOGRAPHIES
Brief Biographical Sketches
Jerome A. Feldman, Principal Investigator: Early training in physics
and mathematics; Ph.D. in computer science from Carnegie-Mellon,
1964. Two years at M.I.T. Lincoln Laboratory. At Stanford since 1966,
leader of Robotics group since 1968. currently Associate Professor of
Computer Science and Associate Director of the Artificial
Intelligence Lab. Research interests include Robotics, associative
processing, inductive inference, and Translator-writing systems.
Thomas O. Binford, Associate Investigator: Received PhD in physics
from the University of Wisconsin, 1965, working with strange particle
reactions, Regge pole and group theory phenomenology. Fulbright
1965-66 Tata Institute of Fundamental Research, Bombay, India: phase
shift analysis of strange particle systems; theory of unstable
particles. At MIT Artificial Intelligence Project 1967-70, working
on representation of shape, region-based analysis system and other
areas of computer vision, systems programming for LISP. Lecturer in
computer science at Stanford University. Research interests are
computer vision, robotics, representation, and perception.
Richard P. C. Paul, Associate Investigator: Undergraduate work in
Engineering Physics at McGill, Masters in Electrical Engineering and
PhD in Computer Science at Stanford University. From 1963 at the
Stanford Linear Accelerator Center as Electronic Engineer. At the
Stanford A. I. Laboratory since 1966 and responsible for manipulator
systems development since 1968. Research interests include Robotics
and digital servo systems.
John McCarthy: BS in Math., Cal Tech, PhD in Math, Princeton, 1951.
With Minsky, organized and directed the Artificial Intelligence
Project at MIT. Joined Stanford as Professor of Computer Science in
1962. Organized and directs the Stanford Artificial Intelligence
Lab. Research interests include nearly all aspects of artificial
intelligence, timesharing systems, mathematical theory of
computation, and representation theory.
Victor Scheinman: Bachelor's in Electrical Engineering, M.I.T.,
Engineer's in Mechanical Engineering from Stanford, 1969. Employed
as a design engineer at Stanford, M.I.T., and Raychem since 1969.
Responsible for the design and construction of computer controlled
arms, as well as other Robotics equipment such as color-wheels, focus
controls, pan-tilt heads, etc.
Lester Earnest: BS in Electrical Engineering, Cal Tech; MS in
Electrical Engineering, MIT, 1960. Two years at MIT Lincoln
Laboratory. Seven years at Mitre Corporation, ending as Head of
Information Systems Department. At Stanford since 1965 as Executive
Officer and Associate Director of the Artificial Intelligence
Laboratory, Research Computer Scientist, and Lecturer in Computer
Science. Responsible for administration and industrial liason.
Curriculum Vitae
JEROME A. FELDMAN
1972
Biographical Information:
Born: December 5, 1938 in Pittsburgh, Pennsylvania, U.S.A.
Nationality: United States
Marital Status: Married, two children
EDUCATION:
B.A. (Physics, with Distinction) Rochester 1960
M.A. (Mathematics) Pittsburgh 1961
Ph.D. (Mathematics, Computer Science) Carnegie-Mellon 1964
HONORS AND SOCIETIES:
Association for Computing Machinery
American Association for the Advancement of Science
National Lecturer, Association for Computing Machinery 1966
Best Session Award, IEEE, August 1970
Fulbright Lecturer, Hebrew University, Jerusalem 1970-71
PROFESSIONAL EXPERIENCE:
Engineer, Westinghouse Electric Corporation 1960-61
Asst. Research Scientist, Carnegie-Mellon U. 1963-64
Staff Member, MIT Lincoln Laboratory 1964-66
Summer Lecturer, U. Pennsylvania 1966
Summer Lecturer, U. Michigan 1967
Asst. Prof., Computer Science, Stanford U. 1966-69
Assoc. Prof., Computer Science, Stanford U. 1969-Present
Visiting Prof. of Computer Science, Hebrew U. 1970-71
Assoc. Dir., Artificial Intelligence Lab., Stanford U. 1972-Present
EDITORIAL, ORGANIZATIONAL, AND CONSULTATION:
Reviewer for Computing Reviews, Journal and
Communications of the ACM, Information and
Control, IEEE Computer Group, various Conferences
and government agencies.
Consultant for IBM, Systems Development Corp.,
Martin-Marietta Corp., Lockheed Missiles and Space
Corp., General Motors, National Institute of Health
Editorial Board, Fundamental Studies in Computer
Science, North-Holland Publishing Company
Lecturer, ACM Professional Development Seminar in
Time-Sharing, 1967
Organizing Committee, Israel Alta Conference,
Ashkelon, February 1971
NATO Conference on Software, Rome,Italy,1969
ACM Conf. on Very High Level Languages, 1974
PUBLICATIONS
Jerome A. Feldman
"Some Regularity Theorems on Kernel Methods of Summation for
Functions of a Complex Variable," Masters' Thesis, U. of Pittsburgh,
1961.
"A Formal Semantics for Computer Oriented Languages", Ph.D.
Dissertation, May 1964, Carnegie-Mellon University.
"Aspects of Associative Processing", TN-1965-13, Lincoln Laboratory,
Massachusetts Institute of Technology, Lexington, Massachusetts.
"A Formal Semantics for Programming Languages", Proceedings of the
International Federation of Information Processing Societies, May
1965.
"A Formal Semantics for Computer Languages and its Applications in a
Compiler-Compiler", Communications of the Association for Computing
Machinery, Vol. 9, No. 1, January 1966, and reprinted in "COMPUTER
COMPILER TECHNIQUES", Auerbach Publishers, 1971.
"An Associative Processing System for Conventional Digital
Computers", (with P.D. Rovner), TN-1967-19, Lincoln Laboratory, MIT,
Lexington, Mass.
"The Compiler-Compiler in a Time-Sharing Environment", (with J.
Curry), Summer Lectures on Advanced Computer Organization, University
of Michigan, June 1967. Also Bit Dropper, September 1967.
"Translator Writing Systems", (with D. Gries), CS 69, Computer
Science Department, Stanford University, June 1967.
"First Thoughts on Grammatical Inference", A.I. Memo 55, Stanford
University, August 1967.
"Translator Writing Systems", (with D. Gries), Communications of the
Association for Computing Machinery, Vol. 11, No. 3, March 1968, and
reprinted in "Computer Compiler Techniques", Auerbach Publishers,
1971.
"The Leap Language and Data Structure", (with P.D. Rovner), Lincoln
Laboratory Memorandum, January 1968. Also in reduced form in
Proceedings of the International Federation of Information Processing
Societies, August 1968.
"Grammatical Inference and Grammatical Complexity", (with others), CS
125, Stanford University, Computer Science Department, Stanford,
California, May 1969.
"An Algol-based Associative Language", (with P.D. Rovner), A.I. Memo
66, Stanford University, 1968. Also Communications of the ACM, Vol.
12, No. 8 August 1969, pp. 439-449. Reprinted in, INTRODUCTION TO
INFORMATION SCIENCE (T. Saracevic, Ed.) Bowker, 1971.
"The Stanford Hand-Eye Project", (with others), Proc. First
International Congress on Artificial Intelligence, Washington, D.C.,
May 1969.
"The Computer Description of Simply Described Scenes", (with R.P.
Paul and G. Falk), A.I. Memo 101, Stanford University. Also in,
PERTINENT CONCEPTS IN COMPUTER GRAPHICS, (J. Nievergelt and M.
Faiman, Eds.), U.of Illinois Press, 1969.
"Machine Intelligence", Review of Numbers I.III of the Machine
Intelligence Series, Information and Control, 14, 490-492, 1969.
"Towards Automatic Programming", Preprints of the NATO Software
Engineering Conference, Rome, Italy, 1969.
"Getting a Computer to See Simple Scenes", IEEE Student Journal,
September 1970.
"A Survey of Grammatical Inference", (with A. Biermann), Proceedings
of International Congress on Pattern Recogniton, Honolulu, January
1971. also in S. Watanbe (Ed.) FRONTIERS OF PATTERN RECOGNITION,
Academic Press, 1972.
"Hebrew University Production Language", (with S. Tsur and D. Lehman)
Hebrew University, Computer Science Department, February 1971.
"A Way of Looking at Things", Hebrew University, Computer Science
Department, February 1971.
"A Laboratory for Hand-Eye Research", (with others), Proceedings
IFIPS International Conference, Lubljana, Yugoslavia, August 1971.
"System Support for the Stanford Hand-Eye Project", (with R. Sproull)
Proceedings Second International Congress on Artificial Intelligence,
London, September 1971, p. 183-189.
"The Use of Vision and Manipulation to Solve the Instant Insanity
Puzzle", (with others), Proceedings Second International Conference
on Artificial Intelligence, London, September 1971, p. 359-364.
"Some Decidability Results on Grammatical Inference and Complexity",
Information and Control, Vol. 20, No. 3, April 1972, p. 244-262.
"On The Synthesis of Finite-State Machines from Samples of their
Behavior", (with A. Biermann), IEEE Transactions on Computers, June
1972, Vol. C-21, No. 6, p. 592-596.
"Automatic Programming", CS 255, Stanford University, Computer
Science Department, Stanford, California, February 1972.
"Total Complexity and the Inference of Best Programs", (with P.
Shields), CS 253, Stanford University, April 1972. Submitted to
Mathematical Systems Theory.
"Recent Developments in SAIL-An Algol Based Language for Artificial
Intelligence", (with others). CS 308, Stanford University, August 1972.
Proc. Fall Joint Computer Conference,Anaheim, Cal.,1972,p.1193-1202.
"Associative Languages" to appear in The Encyclopedia
of Computer Science and Technology, A. Ralston, Ed.
"A Semantics-Based Decision Theoretic Region Analyzer" (with Y.Yakimovsky)
Submitted to Third IJCAI, February,1973
MAJOR PROGRAMMING SYSTEMS DEVELOPED
TRANSLATOR WRITING SYSTEMS
FSL: Additional reference
Itturriaga, R. et.al., "Techniques and Advantages of Using the
formal compiler writing system FSL to implement a Formula Algol
Compiler", Proc. AFIPS Conference 1966, pp. 241-252.
VITAL: Additional Reference
Mondschein, L.F. "VITAL compiler-compiler System Reference Manual",
TN-1967-12. M.I.T., Lincoln Lab., February 1967.
ASSOCIATIVE LANGUAGE SYSTEMS
LEAP: Additional reference
Rovner, P., "The LEAP Users Manual", Mem. 23L-0009, M.I.T., Lincoln
Lab., December 1968.
Swinehart, D. and R. Sproull, "SAIL Users Manual", Operating Note
57.1; Stanford A.I. Project, April 1970.
COOPERATING SEQUENTIAL PROCESSES
HAND-EYE SYSTEM: Additional reference
Pingle, K., "System Manual for Hand-Eye Hackers", Internal
Memorandum, Stanford A.I. Project, June 1971.
DOCTORAL STUDENTS
NAME DEPT. TITLE/TOPIC FINAL ORAL CURRENTLY
F.J. Hilbing Industrial Strategies for 1968 U.S.A.F.
Engineering Paging a Large Academy
Associative Data Base
J.J. Horning Computer A Study of Grammati- 1969 U. of
Science cal Inference Toronto
L.F. Morris Computer Semantic Models and 1969 Syracuse U.
Science Compiler Correctness
G. Falk Computer Computer Interpreta- 1970 Rutgers U.
Science tion of Imperfect
Line Data as a Three-
Dimensional Scene
I. Sobel Electrical Camera Models and 1970 Haifa
Engineering Machine Perception Technion
J.Tenenbaum Electrical Accommodation in 1970 Stanford
Engineering Computer Vision Research
Institute
J.D.Becker Computer An Information 1970 Bolt,
Science Processing Model of Beranek, &
Intermediate Level Newman
Cognition
R. Paul Computer Studies with a 1972 Stanford U.,
Science Computer Controlled A.I. Lab.
Arm
D.Swinehart Computer A One-Level Language 1972 Stanford U.,
Science System A.I. Lab.
A. Gill Electrical Visual Feedback Con- 1972 Israel
Engineering trol of a Mechanical Defense
Arm Establshmnt.
D. Levine Computer Automated Error 1972 Stanford U.
Science Correction of IMSSS
German Sentences
G. Grape Computer Computer Vision 1973
Science Through Sequential
Abstraction
J. Gips Computer Structure of 1973
Science Pictures Applied to
Art and Vision
S.Ganapathy Computer Analysis of Simple 1973
Science Scenes Using Multiple
Views
J. Low Computer Optimization of 1973
Science Data Structures for
Associations
Y.Yakimovsky Computer Knowledge-based, 1973
Science Decision Theoretic
Region Analysis of
Pictures
UNIVERSITY SERVICE
Freshman Seminar, "The Information Revolution" 1967-68
Faculty Senate 1969-70
Presidents Low-Cost Housing Committee 1968-69
Steering Committee, Junior Faculty Forum 1968-69
SWOPSI Seminar, "Computers and Society" 1969-70
Freshman Adviser 1971-72
Committee on Linguistics 1971-72
Lecturer, NSF Summer Program 1971
Faculty Adviser, Student Chapter,
Association for Computing Machines 1966-68
Lecturer, Campus Alumni Conference - May 1970
Departmental Undergraduate Adviser 1972-73
Name: RICHARD P.C. PAUL
Born: June 16, 1936 in Sydney Australia.
Nationality: United States
Marital Status: Married, two children.
EDUCATION:
B.Eng. (Engineering Physics, with Honour) McGill 1963
M.Sc. (Electrical Engineering) Stanford 1968
Ph.D. (Computer Science) Stanford l972
PROFESSIONAL EXPERIENCE:
Electronic Engineer, Stanford Linear Accelerator Center 1963-67
Computer Systems Engineer, Stanford A.I. Laboratory 1967-68
Research Programmer, Stanford A.I. Laboratory 1969-72
Research Associate, Stanford A.I. Laboratory 1972-present
PUBLICATIONS:
"A Multi-pulse Generator for Testing a Radar Beacon", Canadian
Engineering Journal, 1962.
With G. Falk and J. Feldman, "The Computer Representation of Simply
Described Scenes" Memo AIM-101, Computer Science Dept., Stanford
University, Stanford, California.
With Feldman et al, "The Use of Vision and Manipulation to Solve the
Instant Insanity Puzzle", Proc. Second International Joint Conference
on Artificial Intelligence.
"Trajectory Control of a Computer Arm", Proc. Second International
Joint Conference on Artificial Intelligence, 1971.
"Modelling Trajectory Calculation and Servoing of a Computer
Controlled Arm", PhD. Thesis, Computer Science Dept., Stanford
University.
Name: THOMAS O. BINFORD
Born: April 13,1936, Jefferson Twp, Pennsylvania
Nationality: USA
Marital Status:divorced
EDUCATION:
BS physics Pennsylvania State University 1957
PhD physics University of Wisconsin 1965
CURRENT RESEARCH:
Artificial Intelligence Laboratory, Stanford University
laser depth ranging system
representation of shape
recognition of complex objects
efficient method for proximity in n-dimensions
color region analysis
analysis of texture
sensor study
Artificial Intelligence Laboratory, MIT 1967-1970
Topological region analysis
line finder using sequential decision
software for a visual experimentation
recognition of parallelipipeds from incomplete line drawings
two-dimensional shape recognition
line verification
line prediction
POST-DOCTORAL RESEARCH:
Fulbright grant, Tata Institute of Fundamental Research,Bombay India
partial wave analysis of hyperon production by pions;
Regge-pole analysis of Sigma plus production by pions;
theory of unstable particles and symmetry principles
PhD
Strange particle production by pions near 1 Bev/c.
Two new experimental equalities among cross sections;
test of charge independence in hyperon production;
showed adequacy of Regge-pole theory to explain high-energy cross sections;
PUBLICATIONS:
"Structured Descriptions of Complex Objects",R.K.Nevatia and T.O.Binford
Third Int Conf on AI, February, 1973.
Stanford, Calif, 1973.
"Computer Vision", T.O.Binford and J.M.Tenenbaum, Computer, IEEE,
April 1973.
"Sensor Systems for Manipulation", Conference on Remotely Manned
Systems, JPL, Pasadena, Calif, Sept 1972.
"Visual Perception by Computer", Invited paper at IEEE Systems
Science and Cybernetics, Miami, Dec 1971.
With G. Agin, "Representation and Description of Complex Objects", To
be published.
With Feldman, et al, "The Use of Vision and Manipulation to Solve the
Instant Insanity Puzzle", Proc. Second International Joint Conference
on Artificial Intelligence.
With Tenenbaum, et al, "A laboratory for Hand-Eye Research",
IFIP proceedings, August 1971.
"The Vision Laboratory-Part One", MIT AI memo 203.
"The TOPOLOGIST: A Region-Oriented System", (in draft form)
With A. Herskovits, "On Boundary Detection", MIT AI memo 183.
"Display Functions in LISP", MIT AI memo 182.
With Desai, "High Energy Elastic Scattering at Low Momentum Transfers", Physical
Review 138,B1167.
With others, "Experimental Check of Some SU(6) Cross Section Equalities", Physical
Review Letters 14,715.
With others, "Hyperon Production in Pion Interactions", Physical Review (1968).
"Triangle Inequality in Associated Production", Physical Review
(1968).
+ - o
With Anderson, et al, "CP-Nonconserving Decay K ← π π π ", Physical
1
Review Letters 14,475.
o + - o
With Stern, et al, "Absolute Decay Rate for K → π π π and the
2
2
|DI|=1/2 Rule", Physical Review Letters 12,459.
With Lind, et al, "Experimental Investigation of V-A in Leptonic
Lambda Decay", Phys Rev 135,B1483.
DOCTORAL STUDENTS:
G.J.Agin Description and Representation 1972
of Complex Objects
R.K.Nevatia Recognition of Complex Objects 1973
Ruzena Bajcsy (under Prof J. McCarthy)
Computer Identification of Textured 1972
Visual Scenes
T. Garvey Perceptual Problem Solving 1973
VIII. CURRENT SUPPORT
Prof Jerome Feldman is funded by the Office of Computing Activities,
National Science Foundation, and has applied for continuing support
for research in grammatical inference.
The research within the Artificial Intelligence Laboratory related to
the subject of this proposal has been funded by the Advanced Research
Projects Agency, Department of Defense, under contract SD-183.
BUDGET
Research in Robotics and Automation at the Stanford Artificial
Intelligence Laboratory has been supported for several years by the
Advanced Research Projects Agency of the Department of Defense. This
support has supplied most of the necessary capital equipment and
continues to support some of the basic work in this area. The budget
requirements for succeeding projects following this initial one year
proposal will depend on the nature of ARPA support in the future.
EXPLORATORY STUDY OF COMPUTER INTEGRATED ASSEMBLY SYSTEMS
RESEARCH GRANT PROPOSAL BUDGET
ONE YEAR BEGINNING SEPTEMBER 1, 1973
BUDGET CATEGORY
9/1/73-9/1/74
A. SALARIES & WAGES:
1. Senior personnel:
a. Principal Investigator
J. Feldman, Associate
Professor, (2 mos. FTE) $ 3,624
b. Faculty Associate
J. McCarthy, Professor -----
c. Cordell Green, Assistant Professor ----
2. Other Personnel:
a. Research Computer Scientist:
L. Earnest, (2 mos. FTE) $ 4,584
b. Research Associates:
T. Binford, (6 mos. FTE) $ 9,300
L. Paul, (6 mos. FTE) $ 8,820
c. Non-Fac. Professionals (others):
------, Research Programmer,
(12 mos. FTE) $12,000
V. scheinman, Design Engineer,
(6 mos. FTE) $ 8,280
d. Grad Students:
Student Research Assts.:
(3) 1/2 time Acad. Yr.,
(13.5 mos. FTE) $ 9,180
(3) 100% Summer Qtr.,
(9 mos. FTE) $ 6,750
e. Secretarial-Clerical:
(1) Secretary, (12 mos. FTE) $ 7,800
f. Technical:
(1) Electronics Tech.,
(12 mos. FTE) $ 9,600
g. Reserve for Salary Increases
@ 5% per year $ 3,997
_______
TOTAL SALARIES & WAGES $83,935
B. FRINGE BENEFITS:
17.0% 9/1/73 to 8/31/74 $14,269
_______
C. TOTAL SALARIES, WAGES AND
FRINGE BENEFITS (A + B) $98,204
D. CAPITAL EQUIPMENT: $ 6,000
E. EXPENDABLE SUPPLIES AND EQUIP.:
(e.g. Office Supplies, Xeroxing,
Postage, Freight, Consulting,
Honoraria) $ 3,169
F. TRAVEL:
1. Domestic
Local $ 600
Industry Visits $3,600
______
Total Travel $ 4,200
G. PUBLICATION COSTS: $ 400
I. OTHER COSTS:
1. Communications (Telephones) $ 1,000
2. Equipment Rental (Disk) $25,000
________
Total Other Costs $26,000
________
J. TOTAL DIRECT COSTS (A through G) $137,973
K. INDIRECT COSTS:
1. On Campus 47% of Net Total
Direct Costs (H-D) $62,027
_______
L. TOTAL COSTS (G + I) $200,000
_______
_______
REFERENCES
[Alexander] Tom Alexander, "The Hard Road to Soft Automation",
[Anderson 1972] R. Anderson,"Programmable Automation, The Future
of Computers in Manufacturing", Datamation, Dec 1972.
[Agin] G. Agin, "Description and Representation of Curved Objects",
Artificial Intelligence Laboratory, Stanford University, AIM-134,
October 1972.
[Bajcsy 1972] "Computer Identification of Visual Texture"
AIM-180,Artificial Intelligence Laboratory, Stanford University,
October 1972.
[Baumgart 1972a] B. Baumgart, "GEOMED - A Geometric Editor", May 1972.
SAILON-68,Artificial Intelligence Lab, Stanford University,
[Baumgart 1972b] Baumgart, Bruce G., WINGED EDGE POLYHEDRON REPRESENTATION,
AIM 179,Artificial Intelligence Lab, Stanford University,October 1972.
[Binford 1972]"Sensor Systems for Manipulation", Conference on Remotely
Manned Systems, JPL, Pasadena, Calif, Sept 1972.
[Binford 1971] "Visual Perception by Computer", Invited paper IEEE
Systems Science and Cybernetics, Miami, Dec 1971.
[Binford 1970] T.O.Binford, "Ranging by Laser", Internal Report,
Artificial Intelligence Lab, Stanford University, Oct 1970.
[Clowes] M.B. "On Seeing Things" AI Journal,1971.
[Earnest 1967] L.D.Earnest,"Choosing an eye for a Computer";
AI Memo 51, Artificial Intelligence Lab, Stanford University, 1967.
[Falk 1971] G. Falk, "Scene Analysis Based on Imperfect Edge Data", Proc. 2IJCAI,
Brit. Comp. Soc., Sept. 1971.
[Feldman 1972] with others,"Recent Developments in SAIL-An Algol-Based
Language for Artificial Intelligence", (with others).
CS 308, Stanford University, August 1972.
To appear in Proc. Fall Joint Computer Conference, 1972.
[Gill] Gill, Aharon, Visual Feedback and Related Problems in Computer-
Controlled Hand Eye Coordination, AIM 178,October 1972.
[Grape 1969] "Computer Vision through Sequential Abstraction",
Artificial Intelligence Laboratory, Internal Report, 1969.
[Grape 1970] "On predicting and Verifying Missing Elements in Line-Drawings"
Artificial Intelligence Laboratory, Internal Report, March 1970.
[Grape 1973] "Computer Vision through Sequential Abstraction"
Thesis, Computer Science Department, Stanford University, 1973.
[Guzman] A. Computer recognition of three-dimensional objects
in a visual scene. MAC-TR-59 (THESIS), Project MAC, Massachusetts
Institute of Technology, Cambridge, Massachusetts, (December).1968.
[Horn 1971], B.K.P. "The Binford-Horn Linefinder" MIT Vision Flash 16.
[Hueckel] M.H. Hueckel, "An Operator Which Locates Edges in Digitized
Pictures", Stanford Artificial Intelligence Laboratory AIM-105,
December 1969, JACM, Vol. 18, No. 1, January 1971.
[Huffman], D.A. "Impossible objects as nonsense sentences" Machine
Intelligence 6.
[Minsky 1972] "Artificial Intelligence Progress Report", AI Memo 252,
MIT AI Lab, (1972);
[Nevatia 1973] R.K.Nevatia and T.O.Binford, "Structured Descriptions of
Complex Objects", Submitted to Third IJCAI, February 1973.
[Paul] R. Paul, "Trajectory Control of a Computer Arm", 2nd
International Joint Conference on Artificial Intelligence, 1971.
[Paul] R. Paul, "Modelling, Trajectory Calculation and Servoing of a
Computer Controlled Arm", Ph.D. Thesis, Computer Science Dept.,
Stanford University, Sept. 1972.
[Perkins] W.A.Perkins and T.O.Binford, "A Corner Finder for Visual
Feedback", to be published in USA-Japan Vision Conference proceedings,
July 1973.
[Pingle 1972a] Karl K. Pingle, "Hand/Eye Library", Stanford
Artificial Intelligence Laboratory Operating Note 35.1, January,
1972.
[Pingle 1972b] K.K. Pingle and J.M. Tenenbaum, "An Accomodating Edge
Follower", IJCAI-2. Proceedings of the 2nd. International Symposium
on Industrial Robots, May 1972, Chicago, Illinois.
[Quam 1972] L. H. Quam, S. Liebes, Jr., R. B. Tucker, M. J. Hannah,
B. G. Eross, "Computer Interactive Picture Processing", Stanford
Artificial Intelligence Laboratory AIM-166, April 1972.
[Roberts 1963], L.G. Machine perception of three-dimensional solids.
Technical Report No. 315, Lincoln Laboratory, M.I.T. (May)
Also in Optical and Electro-Optical Information Proecesing,
Tippett, J.T. et. al.(Eds.), MIT Press, Cambridge, Mass., 1965,
[Scheinman] V. D. Scheinman, Design of a Computer Manipulator,
Stanford Artificial Intelligence Project, Memo AIM-92, June 1969.
[Sobel] Irwin Sobel, "Camera Models and Machine Perception", Stanford
Artificial Intelligence Project Memo AIM-121, May, 1970.
[Shirai 1971a] "Recognition of Polyhedra with a Range Finder"
Y. Shirai and M. Suwa, Internation Joint Conference on AI, London,
Sept 1971.
[Shirai 1972] "A heterarchical program for Recognition of Polyhedra",
Y. Shirai, Artificial Intelligence Laboratory, MIT, Memo 263.
[Swinehart] D. Swinehart, R. Sproull, "SAIL", Stanford Artificial
Intelligence Laboratory Operating Note 57, November 1969.
[Tenenbaum]
J.M.Tenenbaum,"Accommodation in Computer Vision",
Artificial Intelligence Lab, Stanford University, AIM-134 September 1970.
[Waltz] "Generating Semantic Descriptions from Drawings of Scenes
with Shadows", D.L.Waltz, Artificial Intelligence Laboratory, MIT
AI TR-271.
[Yakimovsky 1972] Y.Yakimovsky and J.A.Feldman,
"A Semantics-Based Decision Theoretic Region Analyzer"
Submitted to Third IJCAI, February,1973
IX. RELEVANT LABORATORY PUBLICATIONS
The following articles published by members of the Stanford
Artificial Intelligence Laboratory are relevant to this proposal.
J. McCarthy, "Computer Control of a Hand and Eye," in Proc. Third
All-Union Conference on Automatic Control (Technical Cybernetics),
Nauka, Moscow, 1967 (Russian).
J. Feldman (with D. Gries), "Translator Writing Systems", Comm. ACM,
February 1968.
J. Feldman (with P. Rovner), "The Leap Language Data Structure",
Proc. IFIP Congress, 1968.
J. McCarthy, L. Earnest, D. Reddy, and P. Vicens, "A Computer with
Hands, Eyes, and Ears", Proc. AFIPS Conf. (FJCC), 1968.
K. Pingle, J. Singer, and W. Wichman, "Computer Control of a
Mechanical Arm through Visual Input", Proc. IFIP Congress 1968.
J. Feldman, G. Feldman, G. Falk, G. Grape, J. Pearlman, I. Sobel, and
J. Tenenbaum, "The Stanford Hand-Eye Project", Proc. International
Conf. on Artificial Intelligence, Washington, D.C., 1969.
J. Feldman (with P. Rovner), "An Algol-based Associative
Language", Comm. ACM, August 1969.
U. Montanari, "Continuous Skeletons from Digitized Images", JACM,
October 1969.
R. Paul, G. Falk, J. Feldman, "The Computer Representation of Simply
Described Scenes", Proc. 2ND Illinois Graphics Conference, Univ.
Illinois, April 1969.
K. Pingle, "Visual Perception by a Computer", in Automatic
Interpretation and Classification of Images, Academic Press, New
York, 1970.
U. Montanari, "A Note on Minimal Length Polygonal Approximation to a
Digitized Contour", Comm. ACM, January 1970.
U. Montanari, "On Limit Properties in Digitization Schemes", JACM,
April 1970.
U. Montanari, "Separable Graphs, Planar Graphs and Web Grammars",
Information and Control, May 1970.
U. Montenari, "Heuristically Guided Search and Chromosome Matching",
J. Artificial Intelligence, Vol. 1, No. 4, December 1970.
G. Falk, "Scene Analysis Based on Imperfect Edge Data", Proc. 2IJCAI,
Brit. Comp. Soc., Sept. 1971.
J. Feldman and R. Sproull, "System Support for the Stanford
Hand-eye System", Proc. 2IJCAI, Brit. Comp. Soc., Sept. 1971.
J. Feldman, et al, "The Use of Vision and Manipulation to Solve the
`Instant Insanity' Puzzle", Proc. 2IJCAI, Brit. Comp. Soc., Sept.
1971.
M. Hueckel, "An Operator which Locates Edges in Digitized Pictures",
JACM, January 1971.
M. Kahn and B. Roth, "The Near-minimum-time Control of Open-loop
Articulated Kinematic Chains", Trans. ASME, Sept. 1971.
U. Montanari, "On the Optimal Detection of Curves in Noisy Pictures",
Comm. ACM, May 1971.
N. Nilsson, "Problem-solving Methods in Artificial Intelligence",
McGraw-Hill, New York, 1971.
R. Paul, "Trajectory Control of a Computer Arm", Proc. 2IJCAI, Brit.
Comp. Soc., Sept. 1971.
K. Pingle and J. Tenenbaum, "An Accomodating Edge Follower", Proc.
2IJCAI, Brit. Comp. Soc., Sept. 1971.
B. Roth, "Design, Kinematics, and Control of Computer-controlled
Manipulators", Proc. 6th All Union Conference on New Problems in
Theory of Machines & Mechanics, Leningrad, Jan. 1971 (in press).
J. Tenenbaum, et, al, "A Laboratory for Hand-eye Research", Proc.
IFIP Congress 1971.
G. Falk, "Interpretation of Imperfect Line Data as a
Three-Dimensional Scene", J. Artificial Intelligence, Vol. 3, No. 2,
1972.
J. Gips, "A New Reversible Figure", Perceptual & Motor Skills, 34,
306, 1972.
X. FILMS
Prints of the following films are available for short-term loan.
1. Art Eisenson and Gary Feldman, "Ellis D. Kroptechev and Zeus, his
Marvelous Time-Sharing System", 16mm black and white with
sound, 15 minutes, March 1967.
2. Gary Feldman, "Butterfinger", 16mm color with sound, 8 minutes,
March 1968.
3. Raj Reddy, Dave Espar and Art Eisenson, "Hear Here", 16mm color
with sound, 15 minutes, March 1969.
4. Gary Feldman and Donald Peiper, "Avoid", 16mm silent, color, 5
minutes, March 1969.
5. Richard Paul and Karl Pingle, "Instant Insanity", 16 mm color,
silent, 6 minutes, August, 1971.
6. Suzanne Kandra, "Motion and Vision", 16 mm color, sound, 22
minutes, November 1972.
7. Karl Pingle, "Automated Pump Assembly by the Stanford Artificial
Intelligence Project," 16mm color, silent, 7 minutes, May 1973.
XI. RECENT REPORTS
The following reports have been published by the Stanford Artificial
Intelligence Laboratory since 1971.
AIM-140, Roger C. Schank, INTENTION, MEMORY, AND COMPUTER
UNDERSTANDING, January 1971, 59 pages.
AIM-141, Bruce G. Buchanan, Edward A. Feigenbaum, and Joshua
Lederberg, THE HEURISTIC DENDRAL PROGRAM FOR EXPLAINING
EMPIRICAL DATA, February 1971, 20 pages.
AIM-142, Robin Milner, AN ALGEBRAIC DEFINITION OF SIMULATION BETWEEN
PROGRAMS, February 1971, 21 pages.
AIM-143. John McCarthy, Arthur Samuel and Artificial Intelligence
Project staff, Edward Feigenbaum and Heuristic Dendral
Project staff, Project Technical Report, MARCH 1971, 80 pages.
(out of print).
AIM-144. Lynn H. Quam, COMPUTER COMPARISON OF PICTURES, May 1971,
120 pages.
AIM-145, Bruce G. Buchanan, Edward A. Feigenbaum, and Joshua
Lederberg, A HEURISTIC PROGRAMMING STUDY OF THEORY FORMATION
IN SCIENCE, June 1971, 41 pages.
AIM-146, Andrei P. Ershov, PARALLEL PROGRAMMING, July 1971, 14 pages.
AIM-147, Robert E. Kling, REASONING BY ANALOGY WITH APPLICATIONS TO
HEURISTIC PROBLEM SOLVING: A CASE STUDY, August 1971,
191 pages.
AIM-148, Edward Ashcroft, Zohar Manna, and Amir Pneuli, DECIDABLE
PROPERTIES OF MONADIC FUNCTIONAL SCHEMAS, July 1971,
10 pages.
AIM-149, Rodney Albert Schmidt, Jr., A STUDY OF THE REAL-TIME CONTROL
OF A COMPUTER-DRIVEN VEHICLE, August 1971, 180 pages.
AIM-150, Robert W. Floyd, TOWARD INTERACTIVE DESIGN OF CORRECT
PROGRAMS, September 1971, 12 pages.
AIM-151, Ralph L. London, CORRECTNESS OF TWO COMPILERS FOR A LISP
SUBSET, October 1971, 41 pages.
AIM-152, A.W. Biermann, ON THE INFERENCE OF TURING MACHINES FROM
SAMPLE COMPUTATIONS, October 1971, 31 pages.
AIM-153, Patrick J. Hayes, THE FRAME PROBLEM AND RELATED PROBLEMS IN
ARTIFICIAL INTELLIGENCE, November 1971, 18 pages.
AIM-154, Zohar Manna, Stephen Ness and Jean Vuillemin, INDUCTIVE
METHODS FOR PROVING PROPERTIES OF PROGRAMS, November 1971,
24 pages.
AIM-155, Jonathan Leonard Ryder, HEURISTIC ANALYSIS OF LARGE TREES AS
GENERATED IN THE GAME OF GO, December 1971, 300 pages.
AIM-156, Kenneth Mark Colby, Franklin Dennis Hilf, Sylvia Weber, and
Helena C. Kraemer, A RESEMBLANCE TEST FOR THE VALIDATION
OF A COMPUTER SIMULATION OF PARANOID PROCESSES,
November 1971, 29 pages.
AIM-157, Yorick Wilks, ONE SMALL HEAD----SOME REMARKS ON THE USE OF
`MODEL' IN LINGUISTICS, December 1971, 17 pages.
AIM-159, J.A. Feldman and Paul C. Shields, TOTAL COMPLEXITY AND
INFERENCE OF BEST PROGRAMS, April 1972.
AIM-160, J. Feldman, AUTOMATIC PROGRAMMING, February 1972, 20 pages.
AIM-161, Y. Wilks, AN ARTIFICIAL INTELLIGENCE APPROACH TO MACHINE
TRANSLATION, February 1972, 44 pages.
AIM-162, Roger Schank, Neil Goldman, Chuck Reiger, Chris Reisbeck,
PRIMITIVE CONCEPTS UNDERLYING VERBS OF THOUGHT,
April 1972, 102 pages.
AIM-163, J.M. Cadiou, RECURSIVE DEFINITIONS OF PARTIAL FUNCTIONS AND
THEIR COMPUTATIONS, April 1972, 160 pages.
AIM-164, Zohar Manna and Jean Vuillemin, FIXPOINT APPROACH TO THE
THEORY OF COMPUTATION, April 1972, 29 pages.
AIM-165, D,A, Bochvar, TWO PAPERS ON PARTIAL PREDICATE CALCULUS,
April 1972, 50 pages.
AIM 166, Lynn H. Quam, Sidney Liebes, Jr., Robert B. Tucker, Marsha
Jo Hannah, Botond G. Eross, COMPUTER INTERACTIVE PICTURE
PROCESSING, April 1972, 40 pages.
AIM-167, Ashok Chandra, EFFICIENT COMPILATION OF LINEAR RECURSIVE
PROGRAMS, June 1972, 43 pages.
AIM-168, Shigeru Igarashi, ADMISSIBILITY OF FIXED-POINT INDUCTION IN
FIRST-ORDER LOGIC OF TYPED THEORIES, May 1972, 40 pages.
AIM-169, Robin Milner, LOGIC FOR COMPUTABLE FUNCTIONS. DESCRIPTION
OF A MACHINE IMPLEMENTATION, May 1972, 36 pages.
AIM-170, Yorick Wilks, LAKOFF ON LINGUISTICS AND NATURAL LOGIC,
June 1972, 19 pages.
AIM-171, Roger Schank, ADVERBS AND BELIEF, June 1972, 30 pages.
AIM-172, Sylvia Weber Russell, SEMANTIC CATEGORIES OF NOMINALS FOR
CONCEPTUAL DEPENDENCY ANALYSIS OF NATURAL LANGUAGE, July 1972,
64 pages.
AIM-173, Gerald Jacob Agin, REPRESENTATION AND DESCRIPTION OF CURVED
OBJECTS, October 1972,
AIM-174, Morris, Francis Lockwood, CORRECTNESS OF TRANSLATIONS OF
PROGRAMMING LANGUAGES -- AN ALGEBRAIC APPROACH, August 1972,
124 p.
AIM-175, Tanaka, Hozumi, HADAMARD TRANSFORM FOR SPEECH WAVE ANALYSIS,
August 1972, 34 pages.
AIM-176, Feldman, J.A., Low, J.R., Swinehart, D.C., Taylor, R.H.,
RECENT DEVELOPMENTS IN SAIL. AN ALGOL-BASED LANGUAGE FOR
ARTIFICIAL INTELLIGENCE, (forthcoming).
AIM-177, Paul, Richard, MODELLING, TRAJECTORY CALCULATION AND SERVOING
OF A COMPUTER CONTROLLED ARM, (forthcoming)
AIM-178, Gill, Aharon, VISUAL FEEDBACK AND RELATED PROBLEMS IN COMPUTER
CONTROLLED HAND EYE COORDINATION, October 1972, 134 pages.
AIM-179, Baumgart, Bruce G., WINGED EDGE POLYHEDRON REPRESENTATION,
October 1972, 46 pages.
AIM-180, Bajcsy, Ruzena, COMPUTER IDENTIFICATION OF TEXTURED VISUAL
SCENES, October 1972, 156 pages.