motion planning for robotic manipulation of deformable linear objects (dlos)
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Motion Planning for Robotic Manipulation of Deformable Linear Objects (DLOs). Mitul Saha and Pekka Isto. Artificial Intelligence Lab Stanford University. Research Institute for Technology University of Vaasa, Finland. Research supported by NSF. - PowerPoint PPT PresentationTRANSCRIPT
Motion Planning for Robotic Manipulation of
Deformable Linear Objects (DLOs)
Mitul Saha and Pekka Isto
Research supported by NSF
Artificial Intelligence LabStanford University
Research Institute for TechnologyUniversity of Vaasa, Finland
• The ability to autonomously manipulate objects is one of most desirable features in a robot. Hence manipulation planning has been an active area of research for the last many decades
–it is difficult to model and predict the deforming nature of deformable objects–struggle in basic motion planning
• There has not been much development in manipulation planning for deformable objects because
Manipulation Planning Research so far…
• So far, manipulation planning research has mainly focused on manipulating rigid objects
• We have been interested in manipulation planning for deformable objects, because a large number of objects that we handle in our daily lives are deformable to some extent
• The ability to autonomously manipulate objects is one of most desirable features in a robot. Hence manipulation planning has been an active area of research for the last many decades
–it is difficult to model and predict the deforming nature of deformable objects–struggle in basic motion planning
• There has not been much development in manipulation planning for deformable objects because
Manipulation Planning Research so far…
• So far, manipulation planning research has mainly focused on manipulating rigid objects
• We have been interested in manipulation planning for deformable objects, because a large number of objects that we handle in our daily lives are deformable to some extent
• The ability to autonomously manipulate objects is one of most desirable features in a robot. Hence manipulation planning has been an active area of research for the last many decades
–it is difficult to model and predict the deforming nature of deformable objects–struggle in basic motion planning
• There has not been much development in manipulation planning for deformable objects because
Manipulation Planning Research so far…
• So far, manipulation planning research has mainly focused on manipulating rigid objects
• We have been interested in manipulation planning for deformable objects, because a large number of objects that we handle in our daily lives are deformable to some extent
• The ability to autonomously manipulate objects is one of most desirable features in a robot. Hence manipulation planning has been an active area of research for the last many decades
–it is difficult to model and predict the deforming nature of deformable objects–struggle in basic motion planning
• There has not been much development in manipulation planning for deformable objects because
Manipulation Planning Research so far…
• So far, manipulation planning research has mainly focused on manipulating rigid objects
• We have been interested in manipulation planning for deformable objects, because a large number of objects that we handle in our daily lives are deformable to some extent
Manipulation Planning for Deformable Linear Objects (DLOs)
GOAL: to develop a motion planner that would enable robots to autonomously manipulate Deformable Linear Objects (ropes, cables, sutures) in various settings.
bowline knot
figure-8 knot
sailing knot
autonomous robotic DLO manipulation
knot tying in daily/recreational life laying/loading cables in industrial settings
suturing in medical surgery
robotdress
Manipulation Planning for Deformable Linear Objects
(DLOs)
• The DLO manipulation problem is extremely challenging for robotics because
o being highly deformable, they can exhibit a much greater diversity of behaviors, which are hard to model and predict
o identifying topological states of DLOs is coupled with some unsolved problems in knot-theory/ mathematics
Interesting
Challenging
• The DLO manipulation problem has a nice structure. It brings together robotics, knot theory, and computational mechanics.
Previous Related Work
“Planning of One-Handed Knotting/Raveling Manipulation of Linear Objects”,IEEE ICRA 2004, Wakamatsu, et. al.
- knot simplified using Reidemeister moves (RM) from knot theory-one robot used to execute the RMs-assumes DLO resting on a plane
Previous Related Work
“Planning of One-Handed Knotting/Raveling Manipulation of Linear Objects”,IEEE ICRA 2004, Wakamatsu, et. al.
Our contribution:
-DLO need not be in a plane-We use more than one robot in coordination-We consider collision constraints (robot-DLO, robot-obstacle)-We consider the physical behavior of the DLO while planning-We consider interaction of the DLO with other objects
- knot simplified using Reidemeister moves (RM) from knot theory-one robot used to execute the RMs-assumes DLO resting on a plane
The Manipulation Problem
How do we define goal configurations?
available robot arms
• Goal configurations are defined in terms of topology instead of exact geometry
Geometrically differentbut
topologically same: Bowline knot
Defining Goal Configurations
while winding, number of wounds
more important
• In knot theory, crossing configuration of a curve is used to characterize its topology
Defining Goal Configurations
planar projection of the DLO central axis
• In knot theory, crossing configuration of a curve is used to characterize its topology
Crossing Configuration: (C1, C2, C3, C4): ((1,-6)-, (-2,5)-, (3,-8)-, (-4,7)-)
crossing:local self-intersections
Defining Goal Configurations
C1: (1,-6)-
C2: (-2,5)-
C3: (3,-8)-
C4: (-4,7)- sign of a crossing
planar projection of the DLO central axis
how to account forinteractions with other objects?
make them partthe DLO
semi-deformable linear object (sDLO)
We take as input the physical model of the DLO in the form of a state transition function f:
Physical modeling of the DLO
Suture model: [Brown, et al., 04] Elastic thread model: [Wang, et al., 05] Nylon thread model: [Dhanik, 05]
Recent successes in computational mechanics:
• Manipulation using 2 cooperating robot arms
Manipulation Tools
• Manipulation using 2 cooperating robot arms
• Use of static sliding supports (“tri-needles”) to provide structural support
Manipulation Tools
• Defining “Forming Sequence”
Forming Sequence: C2, C1, C4, C3
Basis of our Planning Approach
walk along the DLO;crossing “formed” when encountered the second time
• Defining “Forming Sequence”
Forming Sequence: C2, C1, C4, C3
Basis of our Planning Approach
walk along the DLO;crossing “formed” when encountered the second time
C2
C1C4
C3
A DLO topology or knot can be tied, crossing-by-crossing, in the order defined by its “forming sequence”
• Defining “Forming Sequence”
Forming Sequence: C2, C1, C4, C3
Basis of our Planning Approach
• Defining “loop hierarchy”used to determine the placementof
static sliding supports (“tri-needles”)
walk along the DLO;crossing “formed” when encountered the second time
C2
C1C4
C3
A DLO topology or knot can be tied, crossing-by-crossing, in the order defined by its “forming sequence”
Our Manipulation Planning Algorithm
-search the configuration-space using a sampling-based tree
-use forming sequence to bias search
-use physical model to sample new DLO shapes
-use the loop hierarchy to place static sliding supports (tri-needles)
search tree
forbidden region
Our Manipulation Planning Algorithm
-search the configuration-space using a sampling-based tree
-use forming sequence to bias search
-use physical model to sample new DLO shapes
-use the loop hierarchy to place static sliding supports (tri-needles)
search tree
forbidden region
grasping robot fails
Robot A
DLO
Robot A
Robot B
Our Manipulation Planning Algorithm
-search the configuration-space using a sampling-based tree
-use forming sequence to bias search
-use physical model to sample new DLO shapes
-use the loop hierarchy to place static sliding supports (tri-needles)
search tree
forbidden region
Our Manipulation Planning Algorithm
-search the configuration-space using a sampling-based tree
-use forming sequence to bias search
-use physical model to sample new DLO shapes
-use the loop hierarchy to place static sliding supports (tri-needles)
search tree
forbidden region
tri-needles
loop hierarchy
Results
bowline knot
sailing knot
bow
neck-tie
-Planner implemented in C++-Took 15-20 minutes on a 1GB, 1GHz processor to generate manipulation plans for tying popular knots: bowline, neck-tie, bow (shoe-lace), and stunsail-Videos: http://ai.stanford.edu/~mitul/dlo
Results
Results
neck-tie
In the real-life, we have tested the ability of the planner to generate robust plans by tying the popular Bowline knot with various household ropes on a hardware platform with two PUMA robots, using the manipulation plan generated by the planner.
Results
bowline knot
robustness dues to tri-needles
Conclusion
• We have developed a motion planner for manipulating deformable linear objects (such as ropes, cables, sutures) in 3D using cooperating robots.
- it can tie self-knots and knots around rigid objects - unlike in traditional motion planning, goals are topological and not geometric
- we account for the physical behavior of the DLO - it is robust to imperfections in the physical model of the DLO - it is first of its kind (we not aware of any other planner for computing collision-free
robot motions to manipulate a DLO in environments with obstacles) - the implemented planner has been tested both in graphic simulation and in real-life on a dual-PUMA-560 hardware platform
suturing in medical surgery collaboration with General Motors
• Future Plans
Motion Planning for Robotic Manipulation of Deformable Linear Objects (DLOs)
Acknowledgement: Advisory: Jean-Claude Latombe PUMA experiments: Oussama Khatib, Irena, Jaehueng Park, Jin SungPhysical models of ropes: Etienne Burdet, Wang Fei (EPFL)Useful comments: anonymous reviewers
- Tight knots - Semi-tight knots
We focus on two types of common knots:
Crossing Configuration:((1,-6)-, (-2,5)-, (3,-8)-, (-4,7)-)
over
under over
Needle Placement