Relator
Prof.ssa
Reviser
Fiora Pirri
Prof.
Daniele Nardi
Virtual Simulation of the Grasping:
GOLOG Planner and 3D
Simulation for the Problem of Grasping of
SymGEONs recognized by
Neural Network
Andrea Luciani
Summary
• The Manipulation Problem
• The Hand
• Cinematic of the Grasp
• SymGEONs
• Neural Network
• The Planner
The Manipulation Problem
Simulation
Scene reconstruction
Valuation
Realization
Planner
Grasping parameters
calculus
Local
Variables
Planning
Sensing
Visual Sensor
Controller
Control
Tactile Sensor
Actuator
Hand
Object
Camera / Scanner
The Manipulation Problem
Simulator
The Manipulation Problem
State of the Art
Prototypes:
•Salisbury Hand
•Utah/Mit Hand
•16 DOF
•Antagonist tendons
•LL Analogic Control
•HL Control Risc68000 VxWorks Sun
Utah/Mit Hand
The Manipulation Problem
State of the Art
Manipulation Literature
Deterministic techniques
1982, K. Salisbury
Kinematic and Force Analysis of Articolate Hands
1985, M. Mason, K. Salisbury
Robot Hands and the Mecanics of Manipulation
1988, D. J. Montana
The Kinematics of Contact and Grasp
1994, B. Mirtich, J. Canny
Easily Computable Optimum Grasp in 2-D and 3-D
The Manipulation Problem
State of the Art
Manipulation Literature
Euristic Method
1991, S. Stansfield
Robotic Grasping of Unknow Objects: A Knowledge-Based
Approach
1993, S. Caselli, E. Faldella, B. Fringuelli, F. Zanichelli
A Hybrid System for Knowledge-Based Synthesis of Robot
Grasp
Summary
 Manipulation Problem
•The Hand
• Cinematic of the Grasp
• SymGEONs
• Neural Network
• The Planner
The Hand
High Level
The Hand
High Level
11 DOF
The Hand
High Level
Common
Rotation
Axis
The Hand
Primitive Actions:
•Traslatione and Rotation
•Hand Aligment
•Finger Alignment
•Opening
•Finger Positioning
•Force Application
The Hand
Primitiva Actions: Alignement
y
x
Hand
Finger
Summary
 Manipulation Problem
 The Hand
•Cinematic of the Grasp
• SymGEONs
• Neural Network
• The Planner
Cinematic of the Grasp
Grasp Geometry
Cinematic of the Grasp
Contact Models
Contact Type
Outline
Wrench Basis
0
0
 
1 
 
0
0
 
0


Contact
without Friction
Contact with
Friction
Soft-finger
1
0

0

0
0

0

1
0

0

0
0


0
0
1
0
0
0
0
Wb
FC
f 0
0
0

1

0
0

0

0
1
0
0
0
1
0
0
0
0
0
0
f12  f 22   f3
f3  0
0
0

0

0
0

1

f12  f 22   f 3
f3  0
f 4   f3
Cinematic of the Grasp
Cinematic
•Contact Map
0 
 Roci
6mi
6mi
Gi  
W
,
W

R
,
G

R
b
i
 poc  Roc Roc  b
 i
i
i 
k
F0   Gi f ci , f ci  FCi , FCi  R mi
i 1
•Grasp Map
G  G1
Gk  , G  R
FC  R
m
6m
, m  m1 
 mk
Cinematic of the Grasp
Force-Closure:
 Fe R  f c  FC : G f c   Fe
6
G  FC   R
6
Cinematic of the Grasp
Stability  Fe  0 
3
 ROPpi fci  0
i 1
Pp3
x
Pp2
y
y
z


f c3
Co
3


R
f

OPpi ci


f c1
i 1

F0   3


  (( Ppi Co  CoO)  ROPpi ) f ci 
Pp1
 i 1

0


0
3
 3
  ( Pp Co  ROP ) f c  CoO   ROP f c 

i
pi
i
pi
i 
i 1
 i 1

z
x
y
z
Center of Mass
x
f c2
object
Summary
 Manipulation Problem
 The Hand
 Cinematic of the Grasp
•SymGEONs
• Neural Network
• The Planner
SymGEONs
Definition :
Superellipsoid + Deformations
 a1 cos1   cos 2   


1
2
x  ,     a2 cos   sin   


1
 a3 sin  



   ,       ,
2
2
a1 , a2 , a3  R  , 1 ,  2  (0,1]
T
A
P
E
R
I
N
G

 kx

X

z

1


 x

 a3


Y   k y z  1   y



a
 3


k x , k y   0,1
B
E
N
D
I
N
G
 X   1  cos     1  x 


Y  y

1
Z

sin



 x





   0,   ,     z
SymGEONs
Representative ability
SymGEONs
Recognition
Advantages:
•Simmety
•Invariance
•Versatility
Summary




Manipulation Problem
The Hand
Cinematic of the Grasp
SymGEONs
•Neural Network
• The Planner
Neural Network
Functionality
Input: SymGEONs Shape Parameters
Output: Grasp Configurations
Grasp Configuration  Pp1 , Pp2 , Pp3 , Pal , Ca 
Neural Network
Structure
SymGEON
Shape Parameters
a1
a2
a3
kx
ky

1
2
Grasp
Classes
Domain
Structure
F
I
L
T
E
R
NEURAL
Network
12
6
15
Selected
Class
S
E
L
E
C
T
I
O
N
Grasp
Configuration
O
P
T
I
M
I
Z
A
T
I
O
N
Neural Network
Filter
Caracteristic
Zones
Domain
Partition
Neural Network
Optimization
I
Neural Network
Optimization
II
Neural Network
Optimization
III
Summary





Manipulation Problem
The Hand
Cinematic of the Grasp
SymGEONs
Neural Network
•The Planner
The Planner
Input Parameters:

Good _ grasp Pp1 , Pp2 , Pp3 , Pal , Ca , N

Accur  Acc 
Normals  N1 , N 2 , N3 , N 
Force _ grasp  Fg 
Pt _ part  P 
Max _ dim _ mano  M 
Friction  F 
Dia _ palmo  D p 
Obstacle(C , r ,Type, Ext x , Ext y , Ext z ) Dia _ dito  Dd 
The Planner
Primitive Actions:
goAt  L 
approccio _ mano  P 
allinea _ mano  P 
aprimano
allinea _ dita  P0 , P1, P2 , P3 
presa  P1 , P2 , P3 , P4 , P5 , P6 
applica _ forza  F1, F2 , F3 , N1, N 2 , N3 
The Planner
Fluents :
At  P, s 
Near  N , s 
Choosed _ grasp  N , s 
Ready _ app  N , s 
Allineamento  P, s 
Ready _ grasp  N , s 
The Planner
Principals Phases :
•Approach
•Alignement
•Grasp
The Planner
Approach (Path Planning) :
The Planner
Alignement :
proc Allineamento
? Near  N  



Good _ grasp Pp1 , Pp2 , Pp3 , Pal , Ca , N 


Cen _ geo Pp1 , Pp2 , Pp3 , Co ;
allinea _ mano  Co  ;
allinea _ dita Co , Pp1 , Pp2 , Pp3 ; aprimano
endproc


The Planner
Grasp :
proc Grasp
approccio _ mano  P  ;
presa P , P , P , P , P , P ;
applica _ forza  F , F , F , N , N , N
endproc

ap
p
1
p
2
p
3
c
c
1
2
1
c
2
3
3

1
2
3

Summary






Manipualtion Problem
The Hand
Cinematic of the Grasp
SymGEONs
Neural Network
The Planner
End
… to ArmHand2
ALCOR
Autonomous
agents
Laboratory
for
COgnitive
Robotics