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Planning Grasps for Assembly Tasks
arXiv:1903.01631v1 [cs.RO] 5 Mar 2019
Weiwei Wan, Member, IEEE, Kensuke Harada, Member, IEEE, and Fumio Kanehiro, Member, IEEE
Abstract—This paper develops model-based grasp planning
algorithms for assembly tasks. It focuses on industrial endeffectors like grippers and suction cups, and plans grasp configurations considering CAD models of target objects. The developed
algorithms are able to stably plan a large number of high-quality
grasps, with high precision and little dependency on the quality
of CAD models. The undergoing core technique is superimposed
segmentation, which pre-processes a mesh model by peeling it
into facets. The algorithms use superimposed segments to locate
contact points and parallel facets, and synthesize grasp poses
for popular industrial end-effectors. Several tunable parameters
were prepared to adapt the algorithms to meet various requirements. The experimental section demonstrates the advantages
of the algorithms by analyzing the cost and stability of the
algorithms, the precision of the planned grasps, and the tunable
parameters with both simulations and real-world experiments.
Also, some examples of robotic assembly systems using the
proposed algorithms are presented to demonstrate the efficacy.
Index Terms—Grasp synthesis, Grasp planning, Regrasp
I. I NTRODUCTION
T
HIS paper develops algorithms to automatically plan
grasping poses for assembly tasks. It focuses on industrial
end-effectors, and plans grasp configurations for these endeffectors considering CAD models of target objects.
Developing grasp planning algorithms is important to manufacturing using “teachingless” robotic manipulators. Modern
robotic manipulation systems use manually annotated or pretaught grasp configurations to perform certain tasks, which is
not only costly but also difficult to redeploy. Automatic grasp
synthesis or planning algorithms could bypass the annoying
manual work and enable fast redeployment for varying and
changing manufacture. For this reason, lots of studies in
the field of robotic grasp have been devoted to automatic
grasp planning and many grasp planning algorithms have
been developed. These algorithms are able to plan grasps
considering forces and collisions. However, they hardly meet
the requirements of fully automatic manufacturing applications
like bin-picking and assembly. The requirements include but
are not limited to: (1) Large number of candidate grasps:
The grasp planner is expected to provide a large number of
candidate grasp poses for optimization. (2) Stableness: The
grasp planner must have little dependency on the quality of
CAD models. (3) Precision: Object poses do not change much
after being grasped by the planned grasps.
On the other hand, state-of-the-art grasp planning studies
concentrate on theoretical aspects like grasp closures and
qualities, or applicational aspects like grasping using multifinger hands, dexterous hands, and hands with tactile and force
sensors. Grasp planning for popular industrial end-effectors,
Weiwei Wan and Kensuke Harada are with Graduate School of Engineering
Science, Osaka University, Japan. Fumio Kanehiro is with National Inst. of
AIST, Japan. E-mail: wan@sys.es.osaka-u.ac.jp
e.g. parallel grippers and suction cups, is usually ignored
since grasping rigid objects using suction cups and parallel
grippers is considered to be easy and solved. In this paper, we
reinspect this opinion and restudy the grasp planning problem
for parallel grippers. After reviewing previous work, we found
that although grasp planner for parallel grippers had existed for
decades, they do not really meet the requirements of industrial
tasks: Some old-fashioned algorithms could plan grasps for
simple polytopes, but they cannot tackle complicated mesh
models and output satisfying number of grasps; Modern grasp
planning algorithms aim to find stable grasps. They cannot find
a large amount of grasping poses and don’t consider about
their precision. It is difficult to use the planned grasps for
industrial tasks like bin-picking and assembly, etc.
Under this background, this paper develops new grasp
planning algorithms for industrial bin picking and assembly.
The problem setting is formulated as follows. The input
includes: (1) Kinematic models of industrial end-effectors like
suction cups, parallel grippers, and three-finger-one-parameter
grippers; (2) Water-tight models of rigid objects. The output
is: A set of automatically planned grasp configurations. The
algorithms assume: (1) The objects have rigid bodies. Soft or
changeable objects are not considered; (2) The end-effectors
and manipulators are actuated using position control to ensure
fast operation. Tactile or F/T sensors are not available.
The undergoing core technique of the developed algorithms
is superimposed segmentation. The algorithms pre-process a
mesh model by peeling it into facets. Each facet is allowed
to overlap with others and is thus called superimposed segmentation. The overlap and sizes of segments are controlled
by several tunable parameters, which allow users to change
the quality of planned grasps following the requirements of
their applications. The superimposed segments are used to
determine contact and compute parallel facets. Grasp poses
for popular industrial end-effectors are planned considering
the contact and parallel facets.
The developed algorithms could plan a large number of
precise grasps with little dependency on the quality of CAD
models. Object positions change less than 2mm after being
grasped the planned grasps. The computational cost and the
stability of the algorithms in the presence of low-quality
CAD models, as well as the number of planned grasps and
their precision are analyzed in detail in the experimental
section using both simulational and real-world experiments.
The effect of the tunable parameters is analyzed by comparing
the results of varying values. A real dual-arm regrasp and
assembly system is also implemented to show the efficacy of
the proposed algorithms.
The rest of the paper is organized as follows. Section II
reviews related work. Section III discusses the fundamental technique like superimposed segmentation and sampling
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contact points. Section IV presents details of grasp planning
algorithms using the fundamental technique. Section V is the
experimental section. Section VI draws conclusions.
II. R ELATED W ORK
This paper develops model-based grasp planning algorithms
for suction cups and parallel grippers. Accordingly, this section
reviews related studies on grasp theories and grasp planning
of suction cups and parallel grippers, with a special focus on
the preprocessing of mesh models.
A. Grasp theories and grasp planning
Grasp theories study form/force closure and closure qualities. The theoretical studies are applicable to suction cups,
parallel grippers, as well as other robotic hands. Some early
work like [1][2] studied point fingers and polygonal objects,
with later extensions to more realistic scenarios like curved
surfaces and fingers [3][4][5][6][7], considering grasp stability
[8][9][10] and grasp metrics [11][12]. The early theoretical
studies were mostly 2D, and the concentration was to estimate
the stability of grasps and the resistance to external wrenches.
The theoretical studies were extended to 3D polyhedral objects
or mesh models composed of flat faces later, assuming to be
hard point contacts. Examples include [13], [14], etc. Some
other studies optimized the planned grasps [15] using some
quality metrics [16].
Planning grasp poses for real-world objects and real-world
end-effectors are more challenging than the early theoretical
studies. There is a big gap between the computed results
and real-world executions. One has to consider many factors
like contact regions, object surface curvatures, resistance to
torque caused by gravity forces, kinematics of robot hands,
etc., to secure stable and exact grasps. Several previous studies
challenged these difficulties. For example, Wolter et al. [17]
considered the geometry of grippers during the automatic
generation of grasps for 3D rectilinear objects. Jones et al.
[18] considered the parallel faces of a 3D object as well
as the mesh model of a robot gripper to plan two finger
grasps for pick-and-place operations. Liu et al. [19] used the
attractive regions of an object to plan stable grasps. Pozzi et
al. [20] discussed grasp qualities considering the kinematic
structures of underactuated and compliant hands. Shi et al.
[21] considered about environmental constraints as well as the
kinematic constraints of robot hands to plan accessible grasps
for bin-picking and kitting tasks. Li et al. [22] used stretching
ropes (cord geometry) to find the contact of a hand jaw with
object surfaces and hence plan the grasps. Ciocarlie et al.
[23] considered about local geometry and structures at contact
points and modeled friction forces using soft models. Harada
et al. [24] discussed about a gripper with soft finger pads
attached to the finger tips and analyzed object mesh models
considering the depth of contacts. These studies used gripper
models and their kinematic structures to ensure feasibility, and
considered about contact properties by analyzing the meshes
around contact regions.
Our work plans grasp for suction cups and parallel grippers.
For these simple end-effectors, the form/force closure theory
is converted to comparing surface normals at the contacts.
The kinematic constraints, contact, and quality of grasps are
considered in segmentation, sampling, and nested collision
detection. The quality of the planned grasps depends on the
preprocessing of mesh models, which is further reviewed
below.
B. Preprocessing mesh models
Two major approaches to preprocess the mesh models
for grasp planning are (1) primitive fitting and (2) surface
segmentation. The first approach represents mesh models using
a set of shape primitives, and plans grasp by considering
the fitting errors or using pre-annotated grasps. The second
approach represents mesh models using coplanar triangle sets.
Each coplanar segmented triangle set is named a facet and
equals to one constitutional polygon of a polyhedron.
For primitive fitting-based grasp planning, Goldfeder et al.
[25] represented a mesh model using recursive splitting and
fitting of primitive superquadrics [26]. El-Khoury et al. [27] fitted segmented point clouds using primitive superquadrics and
used pre-annotated training sets to learn grasp points from the
fitted models. Xue et al. [28] also used primitive superquadrics
to fit models and plan grasps for Schunk Anthromorph Hands.
Other than superquadrics-based fitting, Miller et al. [29] represented a mesh model using a set of primitive mesh models
like boxes and spheres, and use a set of rules defined on the
primitives to generate grasps for the mesh model. Hueber et
al. [30] fitted mesh models using different levels of primitive
boxes and planned grasp by evaluating the annotated grasps
on the primitives. Bonilla et al. [31] also fitted mesh models
using primitive boxes, and planned grasps using geometric
information extracted from the primitive boxes. Nagata et al.
[32] proposed an interactive method for grasp planning by
assuming shape primitives. Yamanobe et al. [33] defined the
gripping configurations of several shape primitives and used
primitive shape representation to planning grasps for mobile
manipulators. Curtis et al. [34] used primitives to learn grasps.
Instead of explicitly fitting primitives, the authors used learned
grasping knowledge on a set of primitive objects to speed up
the process of planning successful grasps for novel objects.
Harada et al. [35] used cylinders to fit banana point clouds,
and planned robust grasps by analyzing the projections of
the point clouds on the cylindrical axes denoted by the fitted
cylinders. The grasp moduli space proposed by Porkorny et al.
[36] is also a primitive fitting approach. The primitive fitting
approaches do not assure the stability of planned grasps and
exact object poses after grasping. Some further evaluations or
optimizations are needed to make the results practical.
For surface segmentation-based grasp planning, Harada et
al. [24] clustered triangle meshes by using a parameter denoting softness of contacts and implemented grasp planning for
grippers with soft finger pads. Tsuji et al. [37] used multilevel clustering [38] to find the concavity and convexity of
mesh models, and used stress distribution models to plan stable
grasps. Hang et al. [39] also used multi-level clustering to plan
grasps. The difference is their goal was not to find grasping
features. Instead, they use different levels of simplification
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for iterative searching of stable grasps under reachability
constraints. In a later work, Hang et al. [40] extended the
study to fingertip spaces and used multi-resolution contacts
to expedite grasp synthesis. The results of the multi-level
planning were demonstrated in [41] using an Allegro hand.
The hand could gait to different configurations as the weights
of objects change. Some of the primitive fitting approaches
also have a segmentation step, where meshes or point clouds
are segmented for fitting [27][42].
The algorithms developed in this paper use surface segmentation to plan contacts and estimate closure qualities. Unlike
previous work which segmented each triangle into a single
facet or performed multi-level segmentation, the algorithms
allow superimposed segmentation. Each triangle is repeatedly
segmented into different facets, and the overlap and thickness
of facets are controlled by tunable parameters pertaining to
surface normals. By using sampled contact regions on the
superimposed segmentations and leveraging torque resistance,
one may automatically plan a large number of stable and
precise grasp configurations for suction cups and parallel
grippers.
After clustering the first facet, the algorithm initiates a new
seed triangle and repeats the clustering by starting from the
new seed. The routine to initiate a new seed is as follows. The
algorithm scans the surrounding triangles of the previous seeds
and checks the angles between the normals of the previous
seeds and the normals of the surrounding triangles. If an angle
is larger than θf ct , the related triangle is selected as the new
seed. The right part of Fig.2 shows an example. The angle
between the green normal and the purple normal is larger than
θf ct . Thus, the green the triangle with the green normal is
selected as the new seed. The algorithm repeats the clustering
process by using new seed and generates a new facet (the
green facet shown in the right part of Fig.2). θf ct is a tunable
parameter which controls the superimposition of facets.
During clustering, all triangles are repeatedly scanned,
which allows one triangle to be clustered into multiple facets.
The facets could superimpose with each other. On the other
hand, the normals of all previous seeds are compared when
initiating a new seed, which ensures each facet to be unique
and the algorithm to stop properly.
III. P RE - PROCESSING M ESH M ODELS
A. Superimposed segmentation
Superimposed segmentation provides uniform facets. Conventional approaches [38][43] cluster each triangle into a single facet, resulting in uneven facets – Some of them could be
very large, while others are very small. Unlike the conventional
approaches, superimposed segmentation allows one triangle to
be repeatedly clustered to multiple facets, making all facets
uniform (equally large). The clustered facets do not exclusively
occupy the triangles that might also belong to other facets.
Fig. 2: The superimposition is controlled by two parameters.
(1) θpln controls planarity of each facet. (2) θf ct controls the
overlap of facets.
B. Sampling contact points
Fig. 1: Segmenting mesh models into superimposed facets.
Left: Original mesh model. Middle: Results of segmentation.
Right: The facets are superimposed.
The superimposed segmentation is computed as follows.
First, the algorithm initiates a seed triangle and scans the
surrounding triangles of the seed. See the left part of Fig.2
for example. The purple triangle is the seed triangle, and
the algorithm scans the triangles surrounding it. If the angle
between the normal of the seed triangle and the normal of a
nearby triangle is smaller than a threshold θpln , the adjacent
triangle is clustered into the same facet as the seed triangle. In
the figure, the angles between the black arrows and the purple
arrow are equal to or smaller than θpln , the related triangles are
clustered into the purple facet. In contrast, the angles between
the grey arrows and the purple arrow are larger than θpln , the
related triangles are not included. θpln is a tunable parameter
which controls the planarity of a facet.
1) Sampling and distributing: Contact points are computed
by sampling the surface of the object mesh model. The sampling is performed over the whole surface to provide evenly
distributed contact points on the mesh. After sampling, the
sampled points are repeatedly distributed to the superimposed
facets as their contact points. Note that we avoided sampling
individual facets since it only provides evenly distributed
contact points on individual facets, the overall distribution
relies on segmenting methods. In cases where facets are small,
individual sampling may fail to produce contact points.
Fig. 3: Left: Sample over the whole mesh surface. Right: Distribute the samples to each facet to avoid repeated sampling.
Take Fig.3 for example. In the left part, the whole mesh
surface is sampled. In the right part, the sampled points are
distributed to superimposed facets. The surface is sampled
once and the sampled points are distributed to individual
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facets repeatedly. The method ensures the contact points on
each facet have equal density and are evenly distributed. It
is irrelevant to the segmentation methods. Also, the method
distributes the samples to multiple facets without sampling
again. It avoids repeated computation and improves algorithm
efficiency.
2) Removing bad samples: The output of sampling and
distributing cannot be used directly, since the distributed
samples might be (1) near the boundary of facets and (2)
near to each other. In the first case, attaching fingerpads to
the sampled points near facet boundaries lead to unstable
grasps. In the second case, attaching fingerpads to the near
points produces similar grasping configurations, which results
in a large number of similar grasps and is wasteful. To avoid
the problems, we perform two refining processes where the
first one computes the distance between a contact point and
the boundary of its facet. The points with distances smaller
than tbdry will be removed. The second one removes the
contact points that are too close to others. The remaining
contact points after being refined by the first process are
further screened using the Radius Nearest Neighbour (RNN)
algorithm to remove nearby points with a distance smaller than
trnn . Like θpln and θf ct , tbdry and trnn are tunable parameters
of the grasp planner.
Fig. 4: Two refinements that remove the bad contact points.
The first refinement removes the contact points with small
distances to facet boundaries. The second refinement removes
the contact points that are too close to others.
In practice, trnn is determined by the size of finger pads.
An end-effector contacts with objects at a region, instead of
a single point. trnn specifies the radius of the contact region.
It controls the density of planned grasps by removing nearby
candidates.
As a demonstration, Fig.5 shows the process of sampling
contact points using a plastic workpiece shown in Fig.5(a).
Fig. 5: Sampling contact points. (a) The original object. (b)
The sampled contact points. (c) The contact points distributed
to one facet. (d) Removing bad contact points. Especially in
(d), the white points are removed since they are too near to the
boundary. The red points are the results of RNN screening.
3) Stability: The Soft-Finger Contact (SFC) model proposed in [44] is used to estimate the stability of a grasp. The
force and torque exerted by one SFC is expressed as:
f Tt f t +
τn2
≤ µ2 fn2
e2n
(1)
Here, f t indicates the tangential force at the contact. τn2
indicates the torque at the contact. fn indicates the load
applied in the direction of the contact normal. en is the
eccentricity parameter which captures the relationship between
maximum frictional force and moment. Under the Winler
elastic foundation model, en is
R
rµKui (r)dS
max(τn )
(2)
= RS
en =
max(ft )
µKui (r)dS
S
where K is the elastic modules of the foundation over the
thickness of the soft finger pad. S is the contact surface
between the finger pad and the object. r is the distance between
a differential contact point and the center of the contact region.
ui (r) is the depth of the soft penetration. These symbols are
illustrated in Fig.6.
Fig. 6: (a) The soft finger contact model. (b) Curvature of a
facet. (c) The goal is to make sure the object is stable at an
arbitrary pose.
Using R to denote the radius of the the contact curvature,
ui (r) can be represented by
p
ui (r) = R2 − r2 − (R − hmax )
(3)
where hmax is the maximum depth of the soft penetration.
Following the definition of curvature, R could be computed
using max(d/ θi ), where di is the distance between the center of
the ith in the facet and the center of the seed triangle, θi is the
angle between the normal of the ith triangle and the normal of
the seed triangle. The computation is illustrated in Fig.6(b). R
is essentially determined by the geometry of a facet. hmax is
used as a tunable parameter to control the stability of planned
grasps. Since the goal of stability estimation is to make sure
the object is stable at an arbitrary pose (Fig.6(c)), a planned
grasp must meet1
(mgc)2 ≤ en 2 (µ2 fn2 − (mg)2 )
(4)
1 Here, we are considering the worst case where the gripping torque must
resist the largest torque caused by gravity. During manipulation, the largest
external torque appears when gravity direction is perpendicular to vector
−−−−−−−−−−→
contact − com.
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where c is the distance between the com (center of mass) of
the object and the center of contacts. From (2), (3), and (4)
we obtain
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(mgc)2 ≤ ( )2 (2Rhmax − hmax 2 )(µ2 fn2 − (mg)2 ) (5)
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This equation is used to determine the stability of planned
grasps.
examination. The function computes the Euclidean distance
between contact and objcom (the com of the object), and
checks if Eq.(4) is met.
Fig.8 shows the results of grasp planning for a suction
cup and a metal workpiece. A single grasp is shown in
Fig.8(a). All planned results are shown in Fig.8(b). The
red configurations in Fig.8(b) are the grasps deleted by the
checkcollision(eemesh, objmesh) function.
IV. P LANNING THE G RASP C ONFIGURATIONS
Using the superimposed facets and the sampled contact
points, we develop algorithms to plan grasps for suction cups
and parallel grippers.
A. Suction cups
We assume a suction end-effector has only one suction
cup. The grasp planning algorithm for a single suction cup
is a two-step process. In the first step, the algorithm finds
the possible orientations to attach the suction cup to the
sampled and refined contact points. Since the approaching
direction must be perpendicular to the contact region, the endeffector’s orientation is only changeable by rotating around
the approaching direction. The algorithm poses the suction
cup to the contact points from the changeable orientations
and removes the infeasible (collided) grasps. In the second
step, the algorithm examines the resistance of planned suction
configurations to external torques caused by gravity. The
pseudocode of the algorithm is shown in Fig.7.
Fig. 7: The pseudocode of grasp planning for suction cups.
It is a two step process where the first step finds possible
orientations and performs checkcollision, the second step
performs checktorque.
For each contact point, the algorithm discretizes the rotation
around the contact normal into discreteangles, and computes
the rotation matrices. The number of discretized values is
determined by nda . The algorithm poses the eemesh (mesh
model of the end effector) using the computed rotation matrices and checks the collision between the eemesh and the
objmesh (mesh model of the object). Line 3 of the pseudocode
iterates through the discrete orientations. Line 6 poses the
eemesh to a contact with rotation matrix rotmat. Line 7
checks the collision between eemesh and objmesh.
In the second step, the algorithm further examines the resistance to external torques. The function checktorque(contact,
objcom, hmax ) in line 8 of the pseudocode performs the
Fig. 8: Results of grasp planning for a suction cup and a metal
workpiece. (a) One of the planned grasps. (b) All results. The
red grasps collide with the metal workpiece.
B. Two-finger parallel grippers
Grasp planning for two-finger parallel grippers is a threestep process. In the first step, the planner for two-finger parallel
grippers first finds parallel facets and computes candidate
contact pairs by examining the contact points on the parallel
facets. Compared with suction cups which need one contact
point, the planner for two-finger parallel grippers needs two
contact points with opposite contact normals. Thus, the algorithm involves an extra step to prepare the candidate contact
pairs. The second and third steps are similar to suction cups.
In step two, the algorithm finds the possible orientations to
attach the parallel gripper to the candidate contact pairs. In
step three, the algorithm examines the stability of the planned
grasps.
The pseudocode of the grasp planner for two-finger parallel
grippers is shown in Fig.9. In the first block (lines 1 to 8),
the algorithm finds candidate contact pairs. For each pair
of parallel facets, the algorithm initiates a ray that starts
from a contact point on one facet and points to the inverse
direction of the contact normal. It detects the intersection
between the ray and the other facet. If an intersection exists,
the contact-intersection pair is saved as a candidate. Whether
two facets in a pair are parallel is determined by a tunable
parameter θparl (see line 3). In the second block (lines 10 to
22), the algorithm performs collision detection and examines
stability. The algorithm invokes two nested checkcollision
functions in the second block. In the first call (line 14), the
algorithm checks if the stroke of the gripper collides with
the object. Stroke is represented by cylinders which do not
have orientation. In the second call (line 20), the algorithm
checks if the whole hand (both fingers and palm) collides
with the object. The algorithm poses the fingertips to the
center of the contact pairs and performs collision at different
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rotations around the axis formed by the two contacts. During
the process, the planner attaches one finger pad to one position
of a contact pair, and attaches the other finger pad to the
other position of the contact pair. Suppose the two parallel
fingers are f1 , f2 , the contact pair is [contacta , contactb ],
the algorithm attaches f1 to contacta , attaches f2 to contactb ,
−−−−−−−−−−−−−−→
rotates the gripper around the axis contacta − contactb , and
checks the collision between eemesh and objmesh at every
orientation (lines 15 to 20).
Fig.10 shows the results of grasp planning for a Robotiq85
gripper and an electric drill. The CAD model of the drill is
shown in Fig.10(a). The collision between strokes and the
model is detected at line 14 of Fig.9. Fig.10(b.1-3) draw
some results of the collision detection. The red cylinders show
the strokes that collide with the object and are removed to
avoid repeated collision checking at different orientations. The
white cylinders are further examined in line 20 of Fig.9 to
see if there is a collision between the whole hand and the
object. The discretized orientations at one contact pair and
the results of whole-hand collision detection are illustrated in
Fig.10(c). The red hands indicate the collided grasps found by
checkcollision(eemesh, objmesh). The white ones are the
planned grasps.
C. Three-finger parallel grippers
In addition, the planner proposed in this paper is applicable
to three-finger parallel grippers where two fingers are actuated
together against a third finger.
Fig. 9: Pseudocode of grasp planning for two-finger parallel
grippers. stmesh denotes the collision model of a gripper’s
stroke. eemesh denotes the collision model of a gripper.
Fig. 10: Results of grasp planning for a Robotiq85 gripper and an electric drill. (a) The CAD model of the
drill. (b.1-3) Some of the parallel facets and the results of
checkcollision(stmesh, objmesh). The cylinders indicate
the stroke of the gripper. The red ones collide with the drill.
The white ones are collision-free. (b) The discretized grasps at
one contact pair. The red hands indicate the obstructed grasps.
The white ones are the planned grasps.
Fig. 11: Pseudocode of grasp planning for three-finger parallel
grippers.
Fig. 12: Results of grasp planning for a three-finger gripper
and a tube connector. (a) Mesh model of the object. (b.1)
Results of the collision detection on strokes. (b.2) A clear view
of the collision-free strokes of (b.1). (c.1) A collided grasp
configuration found by the second collision checking. (c.2) A
collision-free grasp configuration (it is also one planned grasp
configuration).
The process is similar to grasp planning for two-finger
parallel grippers, except that the two fingers on one side of the
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parallel gripper are treated as a single finger. Its pseudocode
is shown in Fig.11. In the first step, the planner finds parallel
facets and computes candidate contact pairs (not shown in
Fig.11). In the second step, the planer poses the fingertips
to the center of the contact pairs and performs collision
detection at different rotations around the axis formed by the
two contacts (Fig.11). The algorithm also invokes two nested
checkcollision functions. The first invocation checks the
collision of strokes (line 8). The second invocation checks
the collision of the whole hand (line 11). However, different
from the two-finger case, the stroke of a three-finger gripper is
represented by three cylinders, which changes with hand orientation. Thus, both the two invocations are performed under
a specific orientation (both are invoked inside the initialized
by line 3). If the stroke is collision free, the planner attaches
one finger pad to one position of a contact pair and attaches
the center of the other two finger pads to the other position of
the contact pair (line 9). Suppose the three fingers are f1 , f2 ,
and f3 where f1 is at one side of a parallel gripper, f2 and
f3 are on the other side. The contact pair is named [contacta ,
contactb ]. The algorithm attaches f1 to contacta , attaches
f2 +f3
to contactb , and examines the distances between f2 and
2
objmesh, and f3 and objmesh (line 9). The algorithm requires
||f2 -objmesh||<tdct and ||f3 -objmesh||<tdct where tdct is a
tunable parameter. The parameter controls the contact between
the two fingers and the object surface. If the distances meet the
requirements, the algorithm checks the collision between the
gripper and the object (line 10). In the third step, the algorithm
examines the stability (line 12).
Fig.12 shows the results of grasp planning for a threefinger gripper and a tube connector. Some exemplary results
of collision detection with strokes are illustrated in Fig.12(b.1b.2). Fig.12(b.1) shows both the collided (red) and collisionfree (white) strokes. The two-finger side is in the front. The
strokes are rotated around the axis passing through the contact
pair. Fig.12(b.2) is a clear view without the collided ones.
The collision-free strokes are further examined in the second
collision detection. Some exemplary results are illustrated in
Fig.12(c.1) (a collided grasp configuration) and Fig.12(c.2) (a
collision-free grasp configuration).
V. A NALYSIS AND D EMONSTRATIONS
A. Tunable parameters
In the algorithms, seven tunable parameters are prepared
for user configuration. The parameters and their functions are
shown in Table.I. This section analyzes the parameters and
compares the performance of different parameter settings by
comparing the different planned results. In practice, users may
set the parameters according to the needs of their robotic
systems.
Parameter 1: θpln is used to control the planarity of
each facet during the superimposed segmentation. Smaller
θpln leads to flatter and smaller facets. Fig.13(a) shows the
segmented results of the electric drill shown in Fig.10(a) using
different θpln . The facets are drawn with random offsets from
their original position to give a clear view. Each facet is given
a random color. As θpln becomes smaller, facets become flatter
Fig. 13: (a) Results of superimposed segmentation using
different θpln . Each segment is shown in a random color, and is
drawn with a random offset from its original position to give a
clear view. The results show that a smaller θpln leads to flatter
and smaller facets. (b) Results of superimposed segmentation
using different θf ct . The object used for demonstration is the
one in Fig.12(a). The results show that a smaller θf ct leads to
more overlap.
Fig. 14: (a) Results of contact sampling using different tbdry .
The resulting samples are drawn in blue color. The results
show that a smaller tbdry leads to a smaller clearance between
contact samples and facet boundaries. (b) Results of contact
sampling using different trnn . The resulting samples are drawn
in red color. The results show that a smaller trnn leads to
denser contact samples.
and smaller. In the extreme case where θpln = 0◦ , each triangle
is treated as a facet.
Parameter 2: θf ct is used to control the overlap of superimposed segmentation. Smaller θf ct leads to more overlap
between facets. Fig.13(b) shows the segmented results of the
tube connector shown in Fig.12(a) using different θf ct . Like
Fig.13(a), each facet is drawn with a random offset and a
random color. As θf ct becomes smaller, facets become more
overlapped.
Parameter 3: tbdry is used to control the distance between
8
TABLE I: The tunable parameters
Name
Function
Where
θpln
θf ct
tbdry
trnn
hmax
θparl
tdct
nda
Control the planarity of each facet
Control the overlap of facets
Control the distance between contacts and facet boundaries
Control the radius of contact regions
Control the stability of the planned grasps
Control the parallelity of two facets
Control the distances between finger pads and object surfaces
The number of discretized rotation angles around contact normals
Appeared
Appeared
Appeared
Appeared
Appeared
Appeared
Appeared
Appeared
in
in
in
in
in
in
in
in
superimpose segmentation
superimpose segmentation
removing bad samples
removing bad samples
all grasp planners
the planners for two and three-finger grippers
the planner for three-finger grippers
all grasp planners
Fig. 15: Results of parallel facets using different θparl . Arrows indicate facet normals. Two facets with the same arrow color
are parallel. The left part of each subgroup shows two exemplary pairs. The results show that as θparl decreases, less parallel
facets are accepted. The right part of a subgroup shows all pairs. Here, the facets are drawn with random offsets from their
original position to give a good view. As θparl decreases, more pairs are found.
Fig. 16: (a) Results of grasp planning using different hmax .
As hmax decreases, the hand grasps flat facets near the center
of mass to maintain stability. Object: Stanford bunny, the last
model in Fig.17. (b) Results of different tdct . As tdct increases,
more grasps are found. Object: Tube connector, the fourth
model in Fig.17. (c) Results of different nda . A larger nda
leads to denser results. Object: Tube connector.
contacts and facet boundaries. Fig.14(a) shows the results of
contact sampling using the metal workpiece shown in Fig.8(a)
and different tbdry . Only samples on the bottom of the object
are drawn and the resulting samples are drawn in blue color.
A smaller tbdry leads to a smaller clearance between contact
samples and facet boundaries (hence less robust results).
Parameter 4: trnn is used to control the radius of contact
regions or the density of contact sampling. Fig.14(b) shows the
sampling results of the metal workpiece using different trnn .
The samples are drawn in red. A smaller trnn leads to denser
contact samples on the object surface (hence more planned
grasps).
Parameter 5: hmax is used to control the stability of the
planned grasps. An example is shown in Fig.16(a). The object
is the Stanford bunny (the last model in Fig.17). As hmax
decreases, the planner reduces to grasp flat facets near the
center of mass to maintain stability.
Parameter 6: θparl is used to control the parallelity of
the facet pairs in the grasp planning. Values with a larger
offset from 180· lead to more candidate “parallel” facet pairs
to attach the finger pads and hence more planned grasps. On
the other hand, values with larger offsets result in unstable
grasping configurations. Fig.15 shows the parallel facets of a
toy wheel using different θparl . Arrows indicate facet normals.
Two facets with the same arrow color are parallel. Like
Fig.13(a), the facets are drawn with random offsets from
their original position to give a clear view. As θparl becomes
smaller, “parallel” facets become less parallel. Meanwhile, the
number of parallel facets becomes larger.
9
Fig. 17: Computational cost of the proposed algorithms. The rows marked by red and blue shadows denote the most time
consuming process. The values are the average results of ten executions using the following parameter setting: θpln =20◦ ,
θf ct =20◦ , tbdry =2mm, trnn =3mm, trss =50mm, θparl =160◦ , tdct =3mm, nda =8.
Parameter 7: tdct is used to control the distances between
finger pads and object surfaces in the grasp planning for threefinger grippers. Larger values indicate the planner allows a
large difference in distances between finger pads and object
surfaces. In that case, there will be more planned grasps.
Meanwhile, the results are less robust since two fingers cannot
touch object surfaces at the same time. Fig.16(b) shows the
planned grasps of the tube connector shown in Fig.12(b)
using different tdct . As tdct becomes larger, the planned grasp
configurations become denser. The object, which is obstructed
by hands in the figure, is at the same pose as Fig.12(b). The
results also show that when tdct equals 0mm, there are no
lateral grasps. As tdct becomes larger, the number of lateral
grasps increase.
Parameter 8: In addition, nda determines the number of
discretized rotation angles around contact normals. A larger
nda leads to denser results, as is shown in Fig.16(c).
B. Performance
1) Computational costs: The computational costs of planning grasps for various objects using the method are shown
in Fig.17. Six objects are used. From left to right, they are
(1) a bearing housing, (2) a toy wheel, (3) an electric drill,
(4) a tube connector, (5) a metal workpiece, and (6) a plastic
workpiece. The information of these mesh models, including
the number of vertices and the number of triangle faces,
is shown in an upper section of the table in Fig.17. The
details of computational cost, including the time spent on
superimposed segmentation, sampling, removing bad samples
1 (refinement 1 of Fig.4), removing bad samples 2 (refinement
2 of Fig.4), planning contact pairs, and the two nested collision
detection, are shown in a lower section of the table in Fig.17.
The results are obtained by running the algorithms on a
LENOVO ThinkPad P70 mobile workstation. One core of an
Intel Xeon E3-1505M v5 @ 2.80GHz 4 Core CPU is used.
The memory size is 16.0GB. The algorithms are implemented
using python 2.7.11 32 bit. The results are the average values
of ten executions using the following parameter settings:
θpln =20◦ , θf ct =20◦ , tbdry =2mm, trnn =3mm, hmax =1.5mm,
θparl =160◦ , tdct =3mm, nda =8.
The top two time-consuming rows are marked by
red and blue shadows in Fig.4. The first one is
checkcollision(eemesh, objmesh), which required a few
seconds for a few thousand triangles. The results are reasonable as we are performing mesh-to-mesh collision detections.
The second one is “Remove Bad Samples 1” (see Section
III.B.2) and Fig.4). The cost is also reasonable since it
measures the distances of each sampled contact point to the
boundaries of facets.
2) Influence of mesh quality: Fig.18 shows the performance
of the algorithms using a model with different mesh qualities.
The meshes are drawn in Fig.18(1∼8). The number of vertices
and triangles of the meshes decreases from 1 to 8. The two
charts in the figure show the normalized time cost and the
normalized number of planned grasps on these models. Here,
the maximum values are normalized as 1, the other values
are normalized to values between 0 and 1. The maximum
values are used as denominators for normalization. They are
listed in the lower-left corner of the figure. In Chart (a),
the different curves show the changes of various cost. The
meanings of the colors are also shown in the lower-left corner.
All curves in (a) are the results of the same parameter settings:
θpln =20◦ , θf ct =20◦ , tbdry =2mm, trnn =3mm, hmax =1.5mm,
θparl =160◦ , tdct =3mm, nda =8. The two curves in Chart (b)
show the number of planned grasps. The red curve is the
changes of grasp number using the same parameter setting.
The blue curve shows the results using a different θparl value
(θparl =140◦ ). The two curves show that the algorithms are
stable to low-quality mesh models: The number of planned
grasps does not significantly decrease along with reduced
vertices and triangles. For θparl =160◦ , the number of planned
grasps is considered to have similar values in the red shadow
10
Fig. 18: Performance of the proposed algorithms using a model with decreasing mesh qualities. The meshes in 1∼8 have a
decreasing number of vertices and triangles and thus decreasing qualities. The curves in the two charts show the changes in
time costs and the number of planned grasps as the mesh quality decreases.
(spans from 1 to 4). For θparl =140◦ , the number of planned
grasps is considered to have similar values in the blue shadow
(spans from 1 to 6).
3) Precision of the planned grasps: We further measure the
precision of the planned grasps using a Robotiq85 two-finger
parallel gripper and some objects. The experiment settings are
shown in the right part of Fig.19. The objects include (a)
the Stanford bunny, and (b) the bearing housing. AR markers
are attached to the objects to precisely detect the changes of
poses before and after closing the fingers. The grasps with
approaching directions that have less than 40 degree angle
from the vertical direction, as shown in the left part of Fig.19,
are selected as the candidate grasps. They are used to grasp
the objects. The difference in object poses (the difference in
the AR marker’s x and y positions, namely dx and dy ) before
and after closing the fingers are measured as the precision.
Fig. 19: Experimental settings used to examine the precision
of the planned grasps. The left part shows the grasps with
approaching directions that have less than 40◦ angle from the
vertical direction. The changes in object poses before and after
grasping are measured by AR markers shown in the right. Two
objects, a Stanford bunny and a bearing housing, are tested.
The results of the Stanford bunny after grasping are shown
in Fig.20. In the left case, θparl was set to 140◦ , and 6
candidate grasps were found. The maximum change after
grasping using these planned grasps was dx =1.00mm and
dy =4.5mm. When θparl was changed to 160◦ (the right part
of Fig.20), only 1 candidate grasp was found. Its change was
dx =0.25mm and dy =1.00mm.
Fig. 20: Changes in positions after grasping the Stanford bunny
using the planned grasps. The small 3D figures show the
candidate grasps used for comparison. When θparl =140◦ , 6
candidate grasps are found. They are shown in the left 3D
figure. When θparl =160◦ , only 1 candidate grasp is found. It
is shown in the right 3D figure.
The Stanford bunny does not have many graspable parallel
pairs. The available data might not be enough to demonstrate the precision of the planned grasps. Thus, we further
examined the planned grasps of the bearing housing. The
dx and dy before and after grasping the bearing housing
using the planned grasps are shown in Fig.21. The first two
rows of the table are the results using parameter settings:
θpln =20◦ , θf ct =20◦ , tbdry =2mm, trnn =3mm, hmax =1.5mm,
θparl =140◦ , tdct =3mm, nda =8. The lower two rows are the
results using a different θparl . θparl =160◦ . When θparl =140◦ ,
there are 44 candidate grasps. Two failures are encountered
during the experiments using these planned grasps. The failure
cases are marked in red shadows. When θparl =160◦ , there are
32 candidate grasps, and all of them can successfully hold
11
Fig. 21: Changes in x and y after grasping the bearing housing using planned grasps. When θparl =140◦ , 44 candidate grasps
were found. Their poses and precision are shown in the upper section of the table. The maximum positions change after grasping
were dx =2.19mm and dy =1.90mm. They are marked using red frames. Two failures were encountered when grasping using
these planned grasps. They are marked using red shadows. When θparl =160◦ , 32 candidate grasps were found. Their poses
and precision are shown in the lower section. The maximum changes were dx =1.95mm and dy =1.88mm.
Fig. 22: Changes of segmentation and planned grasps of the stanford bunny with different parameter settings. The parameters
included θpln , θf ct , and θparl . At each row, two of these parameters are fixed to 160◦ , the left one decreases from left to right.
the object. The maximum position change after grasping are
(dx =2.19mm, dy =1.90mm), and (dx =1.95mm, dy =1.88mm)
in the two cases respectively. They are marked in red frames.
With the results of these two models, we confirm that the
planned grasps have satisfying precision and are suitable to be
used by assembly routines (e.g. spiral search). Also, we may
change the parameters of the algorithms and seek a balance
between precision and the number of planned grasps according
to the requirements of specific tasks (industrial bin-picking or
assembly).
For readers’ convenience, we summarize the detailed
changes of segmentation and planned grasps of the Stanford
bunny with different parameter settings in Fig.22. In (a), θpln
and θf ct are fixed to 160◦ , θparl decreases from left to right.
The number of planned grasps increases as θparl decreases.
In (b), θf ct and θparl are fixed to 160◦ , θpln decreases
from left to right. The sizes of facets increase significantly
as θpln decreases (The sizes of facets are measured by the
average number of triangles). Meanwhile, as the sizes of facets
increases, the number of planned grasps increases significantly.
12
In (c), θpln and θparl are fixed to 160◦ , θf ct decreases from
left to right. The number of facets decreases slightly as θf ct
decreases. There are no necessary relations between θf ct and
the size of facets, and between θf ct and the number of planned
grasps.
C. Some robotic assembly examples using the grasp planner
The proposed method is implemented as a plugin of an
open source project named PYHIRO (available on Github at
https://github.com/wanweiwei07/pyhiro), where the goal is to
build a system that conducts assembly tasks without human
teach. The input to the system is the CAD models of objects
and kinematic parameters of robots and hands. The output is a
sequence of grasp configurations and robot poses for assembly.
The proposed grasp planning is a pre-processing component
of the project. It prepares pre-annotated grasp configurations
for regrasp planning and motion planning. This subsection
presents some examples of robotic assembly using the systems
and the proposed grasp planning algorithms.
The first example is to use a Kawada Nextage robot
(Kawada Industries, Inc.) to assemble a wheel to a support
shown in Fig.23(a). The hands used were two Robotiq F-85
grippers. The input is the initial positions and orientations of
the wheel and the support in a robot’s workspace (the initial
poses are shown in Fig.23(c)). The system automatically plans
grasps, invalidates collided grasp configurations, and plans
motion sequences using the collision-free grasps. The planned
collision-free grasps for assembling the two objects are shown
in Fig.23(b). Two exemplary planned sequences are shown
in Fig.23(d.1) and (d.2). The real-world execution using one
sequence is shown in Fig.23. The planned grasps are precise
enough to guarantee successful pick-and-place and regrasp.
The second example is to use a dual-arm UR3 robot
(Universal Robots A/S) to pick up a vacuum fastener and
fasten a bolt. The robot plans a bunch of candidate grasps
using the proposed algorithms, and chooses a suitable grasp
to pick up the vacuum fastener in Fig.24(a-c). The planned
grasp is precise enough to align the suction tooltip and fasten
the bolt in Fig.24(d). Like the first example, the gripper used
is the Robotiq F-85 gripper.
The third example is to hand over an electric drill from the
right hand of an HRP5P robot (BNational Inst. of AIST, Japan,
http://y2u.be/ARpd5J5gDMk) to its left hand. The gripper is
a three-finger parallel gripper. The robot could precisely hold
the object using the planned grasps (Fig.25(a-b)), which allows
the left hand to insert its thumb into the narrow space between
the index and middle fingers of the right hand Fig.25(c-d).
VI. C ONCLUSIONS AND F UTURE W ORK
In this paper, we proposed grasp synthesis algorithms that
are efficient, have high precision, and are highly configurable
with several tunable parameters. The algorithms focused on
industrial end-effectors like grippers and suction cups, and
can be used in industrial tasks like bin-picking and assembly.
The efficiency, precision, and configurability of the proposed
method meet the requirements of these tasks. The proposed
planner is demonstrated to be practical by a real-world robotic
assembly task.
We envision our future research focusing on non-continuous
contacts. In this paper, a finger pad is assumed to be continuously in contact with a flat surface of an object, which
makes it difficult to find grasps on non-continuous surfaces,
e.g. grasp the screw threads of a bolt. In the future, we will
use surface simplification, reconstruction, and multi-contact
analysis to plan grasps and challenge the difficulty.
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