hybrid manipulation: force-vision cmput 610 martin jagersand
Post on 19-Dec-2015
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Today:
How to incorporate other sensory feedback.
Focus on tasks where the number of contact constraints increase during manipulations.
Acquire necessary constraint geometry on-line.
Limitations of Visual Control
Accuracy is limited by: Visual tracking and Visual goal specification
(Jagersand et. al. ICRA 97)
Specifying well defined visual encodings can be difficult (Hager, Hespana, Dodds, Jagersand)
Limited to non-occluded settings Not all tasks lend themselves to visual
specification.
Observation:Force constraints natural in tasks
Example:
Inserting a box Sequence of
motions Increasing number
of contact constraints
Preliminaries: Uncalibrated Visual Servoing
Let y = visual observation; x = motor control Linear system model:
y= Linear p-controller
Estimate the Visual-Motor Jacobian
Jêk+1 = Jêk+ É xTÉ x
(É ymeasuredà JêkÉ x)É xT
f (x) ù f (xk) + J (xk)(x à xk)
xç = K J +kyç
Learning Constraint Geometry
Impact force along surface normal:
Sliding motion:
3rd vector:
p1 = jfkjfk
p2 = jî xkjî xk
p3 = p1â p2 = jfkjfk â
jî xkjî xk
Constraint Frame
With force frame = tool frame we get:
Assume frictionless => Can update each time step
Pk+1 =p1p2p3
0
@
1
A =
jfkjfk
jî xkjî xk
jfkjfk â
jî xkjî xk
0
BBB@
1
CCCA
P1
P2
P3
Hybrid Control Law
Let Q Joint -> Tool Jacobian Let S be a switching matrix, e.g. diag([0,1,1]) Velocity control u:
uk = à K vQà1Pà1kSPkQJ à1ek à K cQà1Pà1
k(I à S)Pkfk
Visual part Force part
Accounting for Friction
Friction force is along motion direction! Subtract out to recover surface normal:
Pk+1 =
jfkàpT
2fkp2j
fkàpT
2fkp2
jî xkjî xk
jfkàpT
2fkp2j
fkàpT
2fkp2
âjî xkjî xk
0
BBBBBB@
1
CCCCCCA
Sliding along a Curved Surface
In principle any smooth surface can be followed
Detail of measured forceEstimated surface normals
Estimation Accuracy
The constraint coordinate frame was acquired within a few degrees of the true surface frame