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Motion Planning for Robotic Manipulation of Deformable Linear Objects (DLOs). Mitul Saha, Pekka Isto, and Jean-Claude Latombe. Research supported by NSF, ABB and GM. Artificial Intelligence Lab Stanford University. Objective and Motivation.

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Motion Planning for Robotic Manipulation of Deformable Linear Objects (DLOs)


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motion planning for robotic manipulation of deformable linear objects dlos

Motion Planning for Robotic Manipulation of Deformable Linear Objects (DLOs)

Mitul Saha, Pekka Isto, and Jean-Claude Latombe

Research supported by

NSF, ABB and GM

Artificial Intelligence Lab

Stanford University

objective and motivation
Objective and Motivation
  • Develop a motion planner to aid robot arms perform complex tasks with Deformable Linear Objects (DLOs) like tieing self-knots and knots around objects.
    • Examples of DLO are ropes, strings, surgical sutures, cables etc.

Bowline

knot

Sailing

knot

Figure-8

knot

Surgeon’s knot

Manual knot tying

Robotic knot tying

objective and motivation1
Objective and Motivation
  • Push the state of the art in robotic manipulation
    • Manipulating DLOs is perhaps one of the most challenging tasks in robotics
  • Possible Impact/Applications
    • Could open many new domains for robotics application
      • Enhance manipulation skills of Humanoids assisting humans in their common life activities
      • Automated surgery
the manipulation problem
The Manipulation Problem
  • Defining goal configurations
    • Topological goal vs geometric goal

- This leads to the development of a topological path planner

Geometrically different

but

topologically same:

Bowline knot

the manipulation problem1
The Manipulation Problem
  • Defining goal configurations

- Topological goal (aka “Crossing Configuration”) is defined with respect to a reference plane

Planar projection of the DLO central axis

DLO

Crossings

Crossing Configuration: (C1, C2, C3, C4):

((1,-6)-, (-2,5)-, (3,-8)-, (-4,7)-)

the manipulation problem2
- Reduced alternating

Alternating with embedded slip loops

The Manipulation Problem
  • We focus on two types of common knots:

under

over

over

Crossing Configuration:

((1,-6)-, (-2,5)-, (3,-8)-, (-4,7)-)

slide7
The problem:
    • Start from unwound (State-0) DLO configuration and achieve a configuration and achieve a configuration with desired topology
      • Given:
        • Physics of the

DLO as a

state function f

        • Manipulator arms

A final configuration

with desired topology

Starting configuration

DLO

slide8
Our Planning Approach

- Manipulation using 2 cooperating robot arms

- Use of static sliding supports (“tri-fingers”) to provide structural support

slide9
Defining “Forming Sequence”
    • Knots can be tied, state-by-state, in the order defined by their “forming sequence”

Forming Sequence: C2, C1, C4, C3

A substate

slide10
Defining “hierarchy of components”

The curves in red are “curve-pieces”: c12, c23, c34, c45, c56, c67, c78. The component Co is bounded by {c12, c56}.

I

II

I

II

I

II

I-a

III

III

I-b

slide11
“Hierarchy of components” is used to determine where to place static sliding supports (“tri-fingers”)
slide12
Our “topological” motion planner is based on Probabilistic RoadMaps (PRMs)

-Knot is tied state by state using the

“forming sequence”

Forming Sequence: C2, C1, C4, C3

slide13
Our “topological” motion planner is based on Probabilistic RoadMaps (PRMs)

DLO

Need to find collision free

robot motion that

produces u, the small

random move of the

grasped point

Sample a new

DLO configuration

by randomly perturbing

the grasped point of an

existing configuration

Robot A

Robot A

dQ = J+u + (I - J+J) dq

u: motion of grasp

J : Jacobian

J+: right psuedo-inverse ofJ

dq: small random robot motion

dQ: robot motion producingu

Grasping

robot fails

DLO

Robot B

Robot B

slide14
Our “topological” motion planner is based on Probabilistic RoadMaps (PRMs)
  • Use the “hierarchy of components” to determine when to place
  • static sliding supports (“tri-fingers”)