perm filename NOTES.R[DOC,TOB]5 blob
sn#029379 filedate 1973-03-12 generic text, type T, neo UTF8
00100 The Computerized Factory
00200 Ray Doedecker, Asst Plant General Manager
00300 Systems Manuf Div, IBM, Poughkeepsie, NY
00400
00500 Use of computerized automation at IBM.
00600 Design automation for electronic circuits
00700 check components specs
00800 lists of components
00900 logic diagrams
01000 manufacturing instructions
01100 automatic or hand wire-wrap
01200 pc cards
01300 in-process tests
01400 control assembly machine for discrete wiring
01500 record data on work completed
01600 holes in pc cards
01700 manufacturing computer enclosures
00100 COST ESTIMATES
00200
00300 QUANTITIES OF 1, IGNORE ENGINEERING COSTS
00400 Q1000 INCLUDE ENG COST; ESTIMATES ARE JUDGED CONSERVATIVE BY SCHEINMAN
00500
00600
00700 DEVICE 1973 1978
00800
00900 TV (BW) $300
01000 Q1000 $200
01100
01200 MONITOR $150
01300 Q1000 $70
01400
01500 CAMERA,SOLID STATE
01600 COLOR,256x256 Q1000 $1000 HOPE FOR BETTER(PICTUREPHONE IMPETUS)
01700
01800 PAN/TILT $700
01900 Q1000 $350
02000
02010 TV INTERFACE $1000
02020 Q1000 $300
02030
02100 FOCUS/ZOOM $500
02200 Q1000 $300
02300
02400 LASER $250
02500 Q1000 $175
02600
02700 LASER DEFL $400
02800 Q1000 $250
02900
03000
03100 ARM $3500 WITH 2 FINGER PGRAM HAND
03200 Q1000 $1000
03300
03400 TOUCH&FORCE $1200
03500 Q1000 $600
03600
03700 HAND $1500
03800 3 FINGER,ARTIC $750 TOMOVIC HAND $2500 IN UNITS OF 1
03900
04000
04100 COMPUTER $4000 TI 980A WITH MUL/DIV
04200 Q1000 $3000 $1000
04300
04400 EXTRA MEMORY FOR VISION 8Kx16 BITS, 170x170 4 BITS
04500 Q1000 $1000
04600
04700
04800
04900 SYSTEM COSTS Q1000
05000 MINI MAXI
05100 TV ELEC $470
05200 TV MECH $650
05300 LASER $425
05400 2 ARMS $2000 $2000
05500 TOUCH&FORCE $600 $600
05600 COMPUTER $3000 $3000
05700 EXTRA MEM $1000
05800
05900
06000 TOTALS $5600 $8200
06100
06200 SELLING PRICE $12000 $17000
06300
06400 INCREMENTAL COST ADDED TO UNIMATE (Q1000, ASSUME $25K UNIMATE)
06500 TOUCH&FORCE $600 PRICE $1200 5%
06600 VISION W/MEM $2200 PRICE $4400 16%
06700
06800 A widely quoted capital cost for economically replacing one-shift
06900 assembly line worker in the automobile industry is $20k-$22k. For
07000 isolated assembly stations, the economic cost-benefit analysis
07100 is quite simple; one merely compare cost vs man-shifts. Almost always,
07200 there will be interactions, which we cannot predict. Maintenance, at
07300 one man for twenty manipulators, is not more than equivalent supervisory
07400 staff for workers. Design interactions can raise the cost, but this
07500 is not uniformly true. In many cases, subsidiary improvements can be
07600 made in the interfacing process, which result in additional savings.
00100 GNP
00200 $217 BILLION 30.2% 1970
00300 PRODUCTION WORKERS
00400 MOTOR VEHICLES 580,000 VALUE ADDED $14.5 BILLION
00500 METAL PRODUCTS 575,000 $11.5 BILLION
00600 INSTRUMENTS 385,000 $10.1 BILLION
00700 ELECTRICAL 623,100 $13.4 BILLION
00800
00900 50% OF PRODUCTION IS BY FIRMS WITH GREATER THAN 5000 EMPLOYMENT
01000
01100 ESTIMATE OF SAVINGS IN DOD WITH PROGRAMMABLE AUTOMATION
01200 2.2 BILLION OUT OF 13.3 BILLION 16%
01300
01400 ESTIMATE IN MANUFACTURING SAVINGS WITH PROGRAMMABLE AUTOMATION
01500 $27.6 BILLION OUT OF 492. BILLION 5%
00100 applications of robotics research
00200 automated assembly
00300 computerization of industry
00400 group technology
00500 scheduling and routing
00600
00700
00800
00900
01000 benefits of robotics research
01100 productivity increases
01200 social need for productivity
01300 social service needs
01400 more leisure
01500 unmet material needs: housing,public transit, poverty
01600
01700 areas of robotics most likely to have short term payoff:
01800 manipulation w/ touch
01900 representation of shape and space
02000 perceptual systems-integrated systems
02100
02200 necessity for programmibility in robotics
02300
02400 structure of models
02500
02600 knowability of things
02700 permanence
02800 contiguity
02900 computational requirements
03000 separability of objects
03100 independence of variables
03200 interactions separately described
03300 classification:objects cluster can be classified into similarity classes
03400 structure part/whole
03500 form=function
03600
03700 mechanistic interpretation of affective behavior
03800 eg force someone to do something
03900 symbolic models
04000
04100
04200 information science in production
04300 systematizing control of production
04400 modularization and commonality of parts and operations
04500 description of parts and objects
04600 design automation
04700 analysis of tasks
04800 communication and file storage
04900 scheduling
05000 routing
05100 sensing, monitoring
00100 REPRESENTATION OF SHAPE
00200
00300 what tasks required of a representation
00400 choice of parts
00500 manipulation
00600 avoidance
00700 display
00800 recognition
00900 visual feedback
01000 perceptual strategies
01100 learning
01200 3-d cues
01300
01400 what operations required
01500 display-2 dim proximity
01600 manipulation-center of gravity,center of rotation,length, thickness
01700 avoidance-3 dim proximity, find free space
01800 visual memory-encode shape into memory reference descriptors
01900 recognition-features,visual memory,hypotheses,prediction,verification
02000 geometric relations among parts
02100
02200 simplicity criteria for cross section function
02300 constant
02400 monotonic
02500 continuous
02600 number of maxima
02700
02800 curve representation is topologically inadequate in 2d
02900 surface representation is topologically inadequate in 3d
03000 a)curve is one-dimensional
03100 surface is two-dimensional
03200 b)when do we need higher dimensions-proximity
03300 choice of parts
03400 volume-parts vs surface-parts
03500 area-parts vs curve-parts
03600 c)local realizability
03700 d)dimension arguments
03800
03900 proximity
04000
04100 fabrication operations
04200 form=function heuristics
04300 ease man-machine communication technology transfer
04400 input devices -3d
04500 symbolic input
04600 high-level manipulation
04700 predictive results for perception
04800 initial organization
04900
05000 level of representation
05100 symbolic descriptions--iconic descriptions
05200 example of gravity discussed at coffee
05300 what are we going to do with descriptions
05400 descriptions relative to what we are going to do with them
05500
05600 important properties are not in 2d image: 3d interpretation
05700
05800 system should be a programming language
05900 modular
06000 heterarchical
06100 redundancy, consistency, inconsistency
06200 problem-solving system
06300 approximation of decision trees by statistical estimates
06400
06500 desiderata for representation
06600 generative
06700 levels of detail
06800 3d interpretation
06900 high level
07000 mechanism for similarity
07100 part/whole segmentation structured description
07200
07300 schema, additions, deletions, transformations
07400
07500 volume representation
07600 similarity as a paradigm problem
07700 choice of paradigms
07800 principle of redundancy, hypothesis and verification
07900
08000 significance of shape representation
08100 fabrication operations
08200 form=function
08300 basis for extending Winston's learning paradigm
08400 ease of man-machine communication
08500 input 3d
08600 display
08700 primitives for programming
08800 common vocabulary
08900 useful in initial organization stages
09000 useful for monocular vision
09100
09200 group technology and overall computer control of production
09300 analysis of assembly tasks
09400 description of parts and objects
09500 modularization of process
09600 commonality
09700 automated parts description
09800 task programming for robots
09900 machining descriptions,selective accuracy and monitoring
10000 graphics
10100 visual feedback
10200
10300
10400 partial knowledge--backs of objects invisible in single scene
10500
10600 visual memory
10700
10800 3d representation valuable since accurate predictions can always
10900 be made; not true with 2d representation
11000
11100 for man-machine communication, machine should have same
11200 concepts as man
11300 understands objects in terms of pieces joined
11400 or in terms of fabrication operations (lathe,drill)
11500 pasting
11600 relationships
11700 near
11800 touch
11900 inside
12000 join
12100 cross
12200 overlap
12300 parallel
12400 corresponding
12500 adjacent
12600 above
12700 left,right,in front,behind
12800
12900 see many problems as AI problems
13000 graphics
13100 computer languages
13200
13300 experimentation and learning
13400 three models for learning
13500 statistical inference
13600 deductive/inductive
13700 experimental paradigm of construction of models
13800 independence of variables, specified interactions
13900
14000 depth information as a way of building up the map
14100
14200 bilateral symmetry
14300
14400 three-dimensional cues
14500
14600 joints
14700 sleeves
14800 parallel joints-flat
14900 conservation at joints
15000 intersections as cues to shape
15100 depth measurement techniques
15200 laser
15300 stereo
15400 phase modulation
15500 time of flight
00100 description
00200 computational aspects
00100 Vision as a description process, not vision as a data reduction process.
00200 Not what to forget, but what is worth remembering? In what form is
00300 it worth remembering? Only that which can be made sense of is worth
00400 saving. I.E., descriptions, some images (for motion parallax). Most
00500 images have no worth for later times, since they will not reappear
00600 in just that way, although parts of them might and may be saved as such.
00100 Waltz
00200 edge labels
00300 convex
00400 concave
00500 obscuring
00600 shadow (planar)
00700 crack (planar)
00800 illumination labels
00900 illuminated
01000 shadowed
01100 self-shadowed (facing away from the light source)
01200
01300 consistency conditions
01400 a line must have the same label from vertices at both
01500 ends. Thus, the possible labels for a line are the
01600 intersections of the labels for vertices at both ends.
01700 The possible labels for a vertex are the interpretations
01800 which have consistent line labels for all lines. Possible
01900 interpretations for a cycle of vertices around a region
02000 assign consistent line assignments and illumination
02100 assignments.
02200
00100 Guzman
00200 Roberts used concavities and T's to guess unobscured and obscured
00300 faces of polyhedra in order to fit template models to line-drawing
00400 data. [TOB note: he could have done better by fitting lines than
00500 by fitting points].
00600 Guzman departed from template fitting to separate objects in some
00700 reasonably complex scenes of polyhedra. He used local evidence
00800 from some vertices to link regions. Doubly linked regions were
00900 joined as objects, and any regions linked twice to these objects
01000 were joined, iteratively. Double linking eliminated some errors
01100 in joining, but had to be followed by a joining of regions linked
01200 by a weak and strong link, or single regions linked only to the
01300 body.
01400 The primary links were given by three-line-vertices which were
01500 not a T (indicating obscuration), between regions which had no
01600 concavity (it also inhibited links depending on the background).
01700 A refinement inhibited links if either region was
01800 concave or had an angle of pi at the next vertex along the common
01900 line. Thus the system really depended on concavity,
02000 and on knowledge of the background (given from outside).
02050 We could phrase that condition that a line should be terminated
02075 properly at either end, where terminated properly has the meaning
02087 of each region is strictly convex (less than pi) at the vertex.
02100 Links
02200 Y
02300 Arrow
02400 X
02500 Peak
02600 matching T's
02700 Local Links
02800 Legs
02900 Triangles
03000
03100 SMB links
03200
03300 When did it do badly? Holes and other non-convex features,
03400 accidental alignment, buried vertices.
03500 Objections
03600 did not work very well
03700 was not extendable despite a lot of work
03800 was a cookbook; gave no semantics
03900 required being told background
00100 ROBERTS
00200 3-D SCENES
00300 COMPLETE SYSTEM
00400 USED 3-D MODELS WITH PART/WHOLE SEGMENTATION
00500 BASED ON MATCHING, NOT ON SEGMENTATION BASED ON DEPTH CUES
00600 ASSUMED PERFECT LINE DRAWING
00700 SEPARATION OF HIGH LEVEL AND LOW LEVEL
00800 LINE FINDER AND ORGANIZER PASS-ORIENTED
00900 SUCCESSIVELY MORE GLOBAL
01000 FAIRLY WELL DONE
01100 SOME CAPABILITY WITH OBSCURED OBJECTS
01200
01300
01400
01500 OBTAIN LINES
01600 GRADIENT THRESHOLDED, MAX SAMPLE ON A 4X4 NBHD
01700 T=IMAX/10
01800 CORRELATE WITH LOCAL LINES IN 4 DIRECTIONS
01900 REQUIRE BEST/WORST>3
02000 CONNECT LINE FRAGMENTS
02100 ADJACENT 4X4 NBHDS
02200 LINE WITHIN 23 DEGREES OF BOTH FRAGMENTS
02300 ELIMINATE UNCONNECTED FRAGMENTS
02400 REDUCE LOOPS OF 3 AND 4 FRAGMENTS
02500 LONGER CONNECTIONS-SINGLY CONNECTED
02600 SEQUENTIAL EXTENSION WITH LEAST SQUARES TO DETERMINE CORNERS
02700 RMS ERROR TEST, BACKUP
02800 EXTEND IN OTHER DIRECTION
02900 LINE MERGING AND GAP BRIDGING
03000
03100 POLYGON PROCESSING
03200 LINES
03300 LINK TOGETHER NEARBY END POINTS, ORDERED BY ANGLE
03400 INTERIOR AND EXTERIOR (SUM OF ANGLES)
03500 CONCAVITIES
03600 T'S
03700 SELECT "COMPLETE POLYGONS", W/O CONCAVITIES, W/O STEMS OF T'S
03800 MODELS HAVE 3,4,6 SIDED POLYGONS
03900
04000 MODEL MATCHING
04100 POINT BOUNDED BY COMPLETE POLYGONS N3,N4,N6 ORDERED LIST
04200 LINE BOUNDED BY TWO COMPLETE POLYGONS
04300 COMPLETE POLYGON W/ EXTRA LINE
04400 POINT W/ 3 LINES
04500
04600 DETERMINE THE BEST TRANSFORMATION FROM THE MODEL TO THE
04700 DATA POINTS, MEASURED BY AN RMS ERROR, AND SUCH THAT NO MODEL
04800 POINTS LIE OUTSIDE THE BOUNDARIES OF THE OBJECT, SINCE THEY
04900 WOULD GIVE RISE TO NEW LINES.
05000
05100 DESCRIBES OBJECTS BY SUCCESSIVELY RECOGNIZING PARTS
05200 WHICH ARE AMONG HIS MODELS AND DESCRIBING WHAT IS
05300 LEFT. HIS MODELS ARE RIGHT PRISMS OF 3,4,6 FACES,
05400 IE, BLOCKS, WEDGES, AND HEXAGONAL PRISMS.
05500
05600 TO RELATE A MODEL TO IMAGE PTS, FIND A MATRIX H
05700 AND A DIAGONAL MATRIX D SUCH THAT FOR MODEL PTS A
05800 AND IMAGE PTS B
05900 AH=DB
06000 A=(A11,A12,A13,A14) ROW VECTOR OF FIRST MODEL POINT
06100 (A21,A22,A23,A24) ROW VECTOR OF SECOND MODEL POINT
06200 ................
06300
06400 H=(H11,H12,H13)
06500 (H21,H22,H23)
06600 (H31,H32,H33)
06700 (H41,H42,H43)
06800
06900 B=(B11,B12,B13) ROW VECTOR OF FIRST POINT
07000 (B21,B22,B23) ROW VECTOR OF SECOND IMAGE POINT
07100 ...........
07200
07300 NOTE THAT HE USES POSTMULTIPLICATION AND ROW VECTORS.
07400
07500 THE MATRIX H IS RESTRICTED SOMEWHAT IN AN AD HOC FASHION.
07600 DETERMINE A MATRIX R WHICH IS SUCH THAT
07700 H=RP WHERE P IS A PROJECTION
07800 THEN R=H*INV(P) WHERE INV(P) IS INVERSE OF P.
07900 R IS A 4X4 MATRIX WHICH HAS 15 DEGREES OF FREEDOM
08000 WE WANT TO ALLOW TRANSLATION, ROTATION, AND SCALING
08100 ALONG 3 AXES; NINE DEGREES OF FREEDOM. THE OTHER
08200 SIX DEGREES OF FREEDOM CORRESPOND TO SKEW (CUBE TO PARALLELIPIPED)
08300 AND PERSPECTIVE (COMPRESSION OF ONE END OF MODEL). WE
08400 IGNORE SKEW AND PERSPECTIVE. WE REQUIRE THE PERSPECTIVE COLUMN
08500 OF THE TRANSFORM R TO BE ZERO, AND INCORPORATE ALL PERSPECTIVE IN
08600 A SINGLE PERSPECTIVE TRANSFORM P.
08700 REQUIRING ZERO SKEW IS EQUIVALENT TO REQUIRING THAT THE 3X3 SUBMATRIX
08800 BE ORTHOGONAL (IE A ROTATION).
08900 SUPPORT
09000 THE ANGLES OF PLANES IS KNOWN. EACH PLANE TESTED:
09100 NOT VISIBLE
09200 DOWNWARD Z DIRECTION
09300 APPROXIMATELY HRIZONTAL
09400 CHOOSE BEST PLANE FOR EACH OBJECT. SHOULD BE SAME ORIENTATION
09500 FOR ALL OBJECTS IN PICTURE.
09600
00100 Minsky and Papert AI Memo 252
00200
00300 "blocks world" robotics environment and children's stories better suited
00400 to these studies than puzzle, game, and theorem-proving environments
00500 Thinking is based on the use of SYMBOLIC DESCRIPTIONS and description-
00600 manipulating processes to represent a variety of kinds of KNOWLEDGE--
00700 about facts, about processes, about problem-solving, and about computation
00800 itse