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![Page 1: Harvard University Simple, Robust Grasping in Unstructured Environments Aaron Dollar 1 and Robert D. Howe 2 1 Massachusetts Institute of Technology 2 Harvard.](https://reader036.fdocuments.net/reader036/viewer/2022081514/56649d4b5503460f94a28984/html5/thumbnails/1.jpg)
Harvard University
Simple, Robust Grasping in
Unstructured Environments
Aaron Dollar1 and Robert D. Howe2
1Massachusetts Institute of Technology2Harvard University
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Harvard University
Research Question
• Can the problems associated with robotic grasping in the presence of uncertainty (unstructured environments) be addressed by careful mechanical design of robot hands?
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Harvard University
Our Approach
* “Smart” mechanical design for simplicity of use and robust operation
Durable
Compliant
++
==
Simple+
Robust
Adaptive++
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Harvard University
Our Approach
• Make the hand
– Soft, flexible joints and fingerpads• Minimizes undesirable contact forces
• Gripper passively conforms to objects
How should the compliant hand be designed?
Compliant
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Harvard University
Optimization Goal
• Find the hand configuration that leads to largest Successful Grasp Space with minimum Contact Forces Grasp Space
Object
Contact Forces
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Harvard University
Optimization Goal
• Find the hand configuration that leads to largest Successful Grasp Space with minimum Contact Forces– Simulate the grasping process
• Vary joint angles and stiffness
• Examine effect on performance
Grasp Space
Object
Contact Forces
kbase
kmiddle
φ1
φ2
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Harvard University
Grasp Space
Object
Contact Forces
kbase
kmiddle
φ1
φ2
Simulation Result
Optimum joint rest angles: φ1,φ2=(25º,45º)
Optimum joint stiffness: kbase<< kmiddle
– Optimum across wide
range of object size
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Harvard University
Our Approach
• Incorporate behavior
– More DOFs than actuators• “Underactuated”
• Joints are coupled
– Passively adapts to object shape, location– Simplifies hardware and control
Adaptive
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Harvard University
Underactuated/Adaptive Hands
• Other effective adaptive hands– Barrett Hand
• Most widely used “dexterous”
robot hand– 7 DOF, 4 actuators
– Laval University Hands• E.g. SARAH hand
– 10 DOF, 2 actuators
www.barretttechnology.com
wwwrobot.gmc.ulaval.ca
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Harvard University
Motivation
• How should joints be coupled for good grasping performance?
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Harvard University
Optimization Goal
• Find the hand configuration that leads to largest Successful Grasp Space with minimum Contact Forces– Simulate the grasping process
• Vary torque ratio τ2/τ1
• Examine effect on performance
Grasp Space
Object
Contact Forces
kbase
kmiddle
φ1
φ2
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Harvard University
Grasp Space
Object
Contact Forces
kbase
kmiddle
φ1
φ2
Simulation Result
Optimum torque ratio for poor sensing: τ2/τ1=~1
One actuator per hand performs as well as two!
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Harvard University
Our Approach
• construction
– Unstructured environment unplanned contact– Withstand large forces without damage
Build a durable hand using the design principles from the optimization studies
Durable
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Harvard University
Tendon cable
Soft fingerpads
Viscoelastic flexure joints
Stiff links
Hollow cable raceway
Dovetail connector
2cm
Embedded cable anchor
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Harvard University
Mechanism Behavior
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Harvard University
Grasper Prototype
• 4 fingers
• 8 joints
• 1 actuator
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Harvard University
Tendon Actuation Scheme
• Equal tension on all fingers– Regardless of position, contact
• Adaptable!
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Harvard University
Tendon Actuation Scheme
• Tendons in parallel with compliance much stiffer when actuated– Soft during exploration, acquisition
– Stiff, stable grasp
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Harvard University
Durability
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Harvard University
Hand Properties
• Simple control– 4 fingers, 8 joints
– 1 motor!• Run to stall
– Feed-forward control
• Perform difficult tasks even with 3 positioning DOFs
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Harvard University
Hand Properties
• Simple control– 4 fingers, 8 joints
– 1 motor!• Run to stall
– Feed-forward control
• Perform difficult tasks even with 3 positioning DOFs
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Harvard University
Current Work
• SDM Hand as a prosthetic terminal device– Simple design makes it ideal for both body-
powered or myo-electrically controlled devices– Demonstrated adaptability is desirable– Molded construction can be mass-produced and
made to look realistic
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Harvard University
Acknowledgement
This work was supported by the Office of Naval Research grant number N00014-98-1-0669.
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Harvard University
Grasping in Human Environments
• Large sensing uncertainties– Object size, shape, location, etc. poorly known
• Grasping becomes difficult
• “Unplanned” contact– Large contact forces:
dislodge object, damage gripper– Grasp fails
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Harvard University
Our Overall Approach
• Focus on mechanical design of hands– Compensate for sensing uncertainties and
positioning errors– Durable hardware
• Minimal use of sensing/control
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Harvard University
Grasping in Unstructured Environments
• Traditional approach: Complex hands– Many DOFs and DOAs– Lots of sensing
Utah/MIT handrobonaut.jsc.nasa.gov
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Harvard University
Grasping in Unstructured Environments
• Complex hands = Complicated!– Difficult to control– Expensive– Fragile
Utah/MIT handrobonaut.jsc.nasa.gov
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Harvard University
Grasping in Unstructured Environments
• Complex hands = Complicated!– Difficult to control– Expensive– Fragile
They don’t work reliably
Utah/MIT handrobonaut.jsc.nasa.gov
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Harvard University
Grasping in Unstructured Environments
• How to deal with “poor” sensing?– Errors in positioning,
finger placement– Can’t control contact forces
Grasp will likely be unsuccessfulUtah/MIT hand
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Harvard University
Grasping in Unstructured Environments
• Currently no attractive solution for humanoids and other robots to reliably grasp objects in the human environment!
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Harvard University
SDM Hand
• Simple– Feed-forward control
• Robust!– Immune to impacts– Good performance even
with bad sensing
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Harvard University
Hand Overview
• Slightly larger than human hand– Sized for use in human
environments
• Fabricated by hand using polymer-based Shape Deposition Manufacturing– Aluminum forearm
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Harvard University
Shape Deposition Manufacturing (SDM)
• Build part in layers• Alternate:
• Embed components– Protect fragile parts
• Heterogeneous materialsCourtesy Mark Cutkosky
Part and SupportMaterial Deposition
Material Removal (CNC machining)
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Harvard University
Tendon cable
Soft fingerpads
Viscoelastic flexure joints
Stiff links
Hollow cable raceway
Dovetail connector
2cm
Embedded cable anchor
![Page 35: Harvard University Simple, Robust Grasping in Unstructured Environments Aaron Dollar 1 and Robert D. Howe 2 1 Massachusetts Institute of Technology 2 Harvard.](https://reader036.fdocuments.net/reader036/viewer/2022081514/56649d4b5503460f94a28984/html5/thumbnails/35.jpg)
Harvard University
Fingers
• Single part– No fasteners or
adhesives!
• Lightweight (40g)
• Previous aluminum prototype: 60 parts (40 fasteners), 200g
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Harvard University
• Passively compliant– Large allowable deflections large positioning
errors• 3.5+ cm out-of-plane tip deflection w/o damage
– Low contact forces• Won’t disturb/damage object
• Viscoelastic joints– Damp out max joint deflection oscillations < 1 sec
Finger Properties
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Harvard University
• Hand shape, joint stiffnesses, and joint coupling were chosen based on optimization studies
Hand Configuration Optimization
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Harvard University
Hand Actuation Scheme
• Underactuated/Adaptive– # motors (DOAs) < # DOFs
• Tendon driven– In parallel with springs
• Joints compliant until
tendon tightens
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Harvard University
Hand Actuation Scheme
• Equal tension on all fingers– Regardless of position, contact
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Harvard University
Hand Actuation Scheme
• Equal tension on all fingers– Regardless of position, contact
• Adaptable!
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Harvard University
Hand Properties
• Simple control– 4 fingers, 8 joints, 1 motor!
• Run to stall
– Feed-forward control
• Perform difficult tasks even with 3 positioning DOFs
![Page 42: Harvard University Simple, Robust Grasping in Unstructured Environments Aaron Dollar 1 and Robert D. Howe 2 1 Massachusetts Institute of Technology 2 Harvard.](https://reader036.fdocuments.net/reader036/viewer/2022081514/56649d4b5503460f94a28984/html5/thumbnails/42.jpg)
Harvard University
Hand Properties
• Simple control– 4 fingers, 8 joints
– 1 motor!• Run to stall
– Feed-forward control
• Perform difficult tasks even with 3 positioning DOFs
![Page 43: Harvard University Simple, Robust Grasping in Unstructured Environments Aaron Dollar 1 and Robert D. Howe 2 1 Massachusetts Institute of Technology 2 Harvard.](https://reader036.fdocuments.net/reader036/viewer/2022081514/56649d4b5503460f94a28984/html5/thumbnails/43.jpg)
Harvard University
Hand Properties
• Robust– Immune to impacts
(Also dropped fingers
3x off 50ft. ledge –
no damage!)
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Harvard University
Hand Evaluation
• How do you evaluate grasping performance in an unstructured environment?
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Harvard University
Hand Evaluation
• Experiment 1: – Measure Successful Grasp Space
• “Allowable error” in hand positioning
– Record Contact Forces • Low forces until stable grasp
Object
Contact Forces
Grasp Space
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Harvard University
Experimental Platform
• Hand mounted on WAM robot arm– 3 DOF– No wrist!
• No orientation control
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Harvard University
Experiment 1
• 2 objects– PVC tube (r =24mm)– Wood block (84mm
x 84mm)
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Harvard University
Experiment 1
• Grasp range results– PVC tube
• ±5cm in x – symmetric @ center
• +2cm, -3cm in y
~100% of object size
x
PVC Tube
y
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Harvard University
Experiment 1
• Grasp range results– Wood block
• ±2cm in x – symmetric @ center
• ±2cm in y
~45% of object size
Woodblock
xy
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Harvard University
Experiment 2
• Autonomous grasping across workspace
• Guided by single image– Simple USB webcam
• 640x480 resolution
– Looking down on workspace
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Harvard University
Future Work
• Add wrist, extend range of autonomous objects/tasks
• Investigate the role of sensing in grasping
• Dexterous Manipulation!
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Harvard University
Acknowledgments
• Thanks to the Cutkosky group at Stanford University for advice on SDM fabrication
• Supported by the Office of Naval Research grant number N00014-98-1-0669
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Harvard University
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Harvard University
Call for Papers
Robot Manipulation: Sensing and Adapting to the Real World
Workshop at Robotics: Science and Systems 2007Atlanta, GA, USA
• submission deadline - May 1st • notification of acceptance - May 15th • workshop - June 30th
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Harvard University
iRobot’s PackBot
Durable Robotics
• Rarely addressed in robotics research– Essential for military, space, human environments
– Some locomotion, little manipulation
• In research, durability opens doors– Crashes don’t matter!
– Expands range of tasks that can be attempted
– Speeds implementation – reduces program validation
Utah/MIT hand
Univ. Minnesota’s Scout
Stanford/JPL hand
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Harvard University
Shape Deposition Manufacturing Process
magnets
connectors
Hallsensors
tendoncable
low-frictiontubes
Pockets with embedded componentsA CB
ED F
Dam material
Stiff polymer
New pockets
Soft polymersSoft polymers
Stiff polymer Complete fingers
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Harvard University
SDM robots
• Sprawl family of robots
• RiSE robots
[Introduction] Grasper Design Grasper Evaluation
Courtesy of Mark Cutkosky Courtesy of Mark Cutkosky
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Harvard University
Hand Actuation Scheme
• Underactuated/Adaptive– # motors < # DOFs
• Tendon driven– In parallel with springs
• Joints compliant until
tendon tightens
Optimum joint coupling:
~1:1 torque ratio
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Harvard University
Design Optimization
Object
RobotMotion
• Scenario (i.e. arbitrary assumptions)– Object ≈ circle (planar)– Sense approximate object location
(e.g. vision)– Move straight to object – Detect contact, stop robot– Close gripper
• Simple (simplest?) gripper– Two fingers– Two joints each – Springs in joints
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Harvard University
Configuration Optimization
• Kinematics and stiffness design optimization – Simulate finger deflection as
object grasped – Varied joint rest angles
and joint stiffness ratio– Find largest successful Grasp
Space– Find maximum Contact Force
Grasp Space
Object
Contact Forces
RobotMotion
kbase
kmiddle
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10 25 40 55 70 85
10
25
40
55
70
85
1
0
0.2
0.4
0.6
0.8
1
0
0.2
0.4
0.6
0.8
1
0
0.2
0.4
0.6
0.8
1
10
25
40
55
70
85
2
10 25 40 55 70 85
1
10 25 40 55 70 85
1
10
25
40
55
70
85
k1/k2= 10
r/l=0.1
top contour = 0.45
top contour = 0.85
top contour = 0.95 top contour = 0.95 top contour = 0.95
top contour = 0.85 top contour = 0.85
top contour = 0.45 top contour = 0.45
(xc)max/l
max value = 0.99 max value = 0.99 max value = 0.99
max value = 0.86 max value = 0.86 max value = 0.86
max value = 0.46 max value = 0.46 max value = 0.46
A B
2
2
k1/k2= 1 k1/k2= 0.1
r/l=0.5
r/l=0.9
(xc)max/l
(xc)max/l. .
Configuration Optimization• Combine results:
Grasp range and Contact force• Optimum joint rest angles:
φ1,φ2=(25º,45º) • Optimum joint stiffness:
kbase<< kmiddle
Grasp Space
Stiff base jointStiff middle joint Equal joint stiffness
Middle Joint Rest Angle
10 25 40 55 70 85
10
25
40
55
70
85
1
0
0.2
0.4
0.6
0.8
1
0
0.2
0.4
0.6
0.8
1
0
0.2
0.4
0.6
0.8
1
10
25
40
55
70
85
2
10 25 40 55 70 85
1
10 25 40 55 70 85
1
10
25
40
55
70
85
k1/k2= 10
r/l=0.1
top contour = 0.45
top contour = 0.85
top contour = 0.95 top contour = 0.95 top contour = 0.95
top contour = 0.85 top contour = 0.85
top contour = 0.45 top contour = 0.45
(xc)max/l
max value = 0.99 max value = 0.99 max value = 0.99
max value = 0.86 max value = 0.86 max value = 0.86
max value = 0.46 max value = 0.46 max value = 0.46
A B
2
2
k1/k2= 1 k1/k2= 0.1
r/l=0.5
r/l=0.9
(xc)max/l
(xc)max/l. .
Base Joint Rest Angle
Grasp Space
Object
Contact Forces
kbase
kmiddle
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Joint Coupling Optimization
Object
RobotMotion
• Object: – circle (planar), “unmovable”
• General scenario:1. Sense approximate object location
(e.g. vision)2. Move straight to object 3. Detect contact, stop robot4. Close gripper
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Actuation Scheme
• To enable analysis, analyzed tendon-driven finger– Results of study apply to other
transmission methods
• One actuator per hand (4 joints)
Introduction [Grasper Design] Grasper Evaluation
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Grasp Scenario
[Introduction] Grasper Design Grasper Evaluation
Initial contact, no deflection
Begin actuationFinger 2 contact,force application
Object enclosure
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Actuation Optimization
• Vary joint torque ratio (distal:proximal)– Tendon routing + joint stiffnesses determine
joint torque ratio
• Find maximum Grasp Space, minimum Contact Forces
Introduction [Grasper Design] Grasper Evaluation
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Contact Force
Large ObjectSmall Object
Object location(distance
from hand center)
Torque Ratio middle/base
Grasp fails
Simulation Results
Tradeoff between low forces and large grasp range
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Analysis of Results
• Consider the quality of sensory information– E.g. don’t need large grasp space when sensing
is good large torque ratio, low forces
• Assume a normal distribution of object position from expected position– Low σ for good sensing– High σ for poor sensing
[Introduction] Grasper Design Grasper Evaluation
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Weighted Force
• Average over position and object radius
• Forces near expected position weighted more strongly
[Introduction] Grasper Design Grasper Evaluation
Better performance(lower forces)
torque ratio
forc
e qu
ality
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Weighted Grasp Space
• Weighted by probability of object within the grasp space
[Introduction] Grasper Design Grasper Evaluation
torque ratio
Better performance
Gra
sp s
pace
qua
lity
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Weighted Product
Noisy sensing
Good sensing
X
X
Optimum Torque Ratio:
• Product of the two quality measures
torque ratio
Betterperformance
Pro
duct
of
qual
ities
![Page 71: Harvard University Simple, Robust Grasping in Unstructured Environments Aaron Dollar 1 and Robert D. Howe 2 1 Massachusetts Institute of Technology 2 Harvard.](https://reader036.fdocuments.net/reader036/viewer/2022081514/56649d4b5503460f94a28984/html5/thumbnails/71.jpg)
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Underactuated/Adaptive Hands
• Other effective adaptive hands– Barrett Hand
• Most widely used “dexterous”
robot hand– 7 DOF, 4 actuators
– Laval University Hands• E.g. SARAH hand
– 10 DOF, 2 actuators
[Introduction] Grasper Design Grasper Evaluation
www.barretttechnology.com
wwwrobot.gmc.ulaval.ca
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Motivation
• How should joints be coupled for good grasping performance?– Very little research in this area
• Kaneko et al. 2005 – results particular to one specific grasper and task
• Birglen and Gosselin 2004 – Very good general framework for finger analysis, little consideration of object, grasping task
[Introduction] Grasper Design Grasper Evaluation
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Call for Papers
Robot Manipulation: Sensing and Adapting to the Real World
Workshop at Robotics: Science and Systems 2007Atlanta, GA, USA
• submission deadline - May 1st • notification of acceptance - May 15th • workshop - June 30th
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Analysis
• Initial contact and
beginning Actuation
ii i
ik
for i=2,3,4
11
1
sin
coscx r
a
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Analysis
• Contact on link 3
3 1a a
3 3 3sin cos 0cont cont cr a x
xc
φ1
k2
k1
ψ3cont
a1a3
ψ4
ψ2
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Analysis
• Contact on outer links
12 4
1
2 tancont cont
r
l a
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Overall Quality Measure
• Good sensing– Average doesn’t make
sense
– No predetermined xt
• Can target according to object size
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Overall Quality Measure
• Good sensing– Take maximum for
each torque ratio
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Overall Quality Measure
• Good sensing– Take maximum for
each torque ratio
Optimum at ~ 1:1
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Grasper Fabrication Process
magnets
connectors
Hallsensors
tendoncable
low-frictiontubes
Pockets with embedded componentsA CB
ED F
Dam material
Stiff polymer
New pockets
Soft polymersSoft polymers
Stiff polymer Complete fingers
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Mechanism Behavior
• Very low tip stiffness– x=5.85 N/m– y=7.72 N/m– z=14.2 N/m
• Large displacements
• Impact resistant!