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Global Journal of Researches in Engineering: A
Mechanical and Mechanics Engineering
Volume 22 Issue 2 Version 1.0 Year 2022
Type: Double Blind Peer Reviewed International Research Journal
Publisher: Global Journals
Online ISSN: 2249-4596 & Print ISSN: 0975-5861
Passive Sensing Jaw for Grasping and Orienting
By Alessandro Luchetti, Mariolino De Cecco, Matteo Perotto,
Paolo Bosetti, Daniele Fontanelli, Luigi Palopoli, Fabiano Zenatti,
Matteo Zanetti, Luca Maule & Mattia Tavernini
University of Trento
Abstract- The goal of this work is to present an innovative design for a smart robotic gripper,
which is able to grasp different randomly deployed prismatic and cylindrical packages and orient
them through a mechanically passive alignment system with sensing capability. It consists in a
new concept of end-effector combined with an ad-hoc path planning for aligning residual worst
cases. The system uses gravity and an angular sensor embedded into the gripper to detect the
object orientation and, if necessary, formulate a control strategy to align it before the release
phase. An initial screening experiment was executed to find the parameters that most influence
the alignment angle and execution time. Two worst-case pack ages were tested in different
working conditions. The results show that the percentage of success of the system is high even
in the worst operating conditions.
GJRE-A Classification: FOR Code: 091399
PassiveSensingJawforGraspingandOrienting
Strictly as per the compliance and regulations of:
Passive Sensing Jaw for Grasping and Orienting
Abstract-
The goal of this work is to present an innovative
design for a smart robotic gripper, which is able to grasp
different randomly deployed prismatic and cylindrical
packages and orient them through a mechanically passive
alignment system with sensing capability. It consists in a new
concept of end-effector combined with an ad-hoc path
planning for aligning residual worst cases. The system uses
gravity and an angular sensor embedded into the gripper to
detect the object orientation and, if necessary, formulate a
control strategy to align it before the release phase. An initial
screening experiment was executed to find the parameters
that most influence the alignment angle and execution time.
Two worst-case pack ages were tested in different working
conditions. The results show that the percentage of success of
the system is high even in the worst operating conditions.
I. Introduction
icking, aligning, and placing objects of different
shapes and sizes is a very common task in the
automation industry. The most commonly used
interfaces for picking involve vacuum force obtained via
suction cups and sponges or mechanical friction
provided by soft and rigid jaws [1], which have the
higher capability to grip objects of different shapes,
volumes, and masses without changing the jaw shapes.
The jaws can be designed in different configurations
and actuated either by pneumatic systems, which are
noisy and expensive as they require a vacuum line, or by
mechanical systems.
Parallel configuration is the most commonly
used for picking objects of standard geometries, since
higher dexterity configurations require a complex and
expensive adaptive control strategy. One example of
high dexterity configuration is the dexterous hand
presented in [2], [3] and [4]. Examples of the complexity
of the control strategy for this kind of solutions are
reported in [5] for rolling approach, in [6] for sliding
approach and [7] for gaiting approach. The choice of
using a parallel gripper is justified in the manufacturing
field by Bracken [8], who proposed a geometrical
classification of parts to be gripped into six shape
categories (i.e., spherical, rectangular, cylindrical,
triangular, holed and flexible) and stated that the gripper
able to deal with most shapes is the parallel two-jaw
gripper. Assuming that a robotic system is composed of
a robot and a mechanical parallel gripper, it is possible
to solve the alignment problem with two strategies:
using a high degree of-freedom manipulator equipped
with a gripper that has no alignment capability or
performing the alignment by using the gripper rather
than robot kinematics. Holladay et al. [9] demonstrated
that the task can be solved in a shorter time and with a
smaller work space using the second approach.
The orientation problem using only the gripper
can be solved in several ways, but the most commonly
used is pivoting [10]. It consists in closing the gripper
jaws in such a way that the object can rotate around the
axis passing through the contact points. Rao et al. [11]
demonstrated the effectiveness of this orienting
technique by making four degree of freedom robot to
move a polyhedral part in space (along all the object’s
degrees of freedom). This approach takes advantage of
gravity to complete the alignment so that the alignment
system can be defined as passive. It also introduces a
constraint on the gripping distance from the object’s
center of mass. Making the realignment system to be
active allows to get rid of this constraint and to control
the alignment angle, but the introduction of additional
hardware decreases reliability while increasing costs.
In this paper, we propose and validate a new
design with a passive realignment system to be
integrated into parallel mechanical grippers. In addition,
in pick and place operations it can be necessary to
choose if the object has to be realigned or not, a
problem that is addressed using a passive mechanical
system integrating an angular sensor monitoring in real-
P
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Alessandro Luchetti α, Mariolino De Cecco σ, Matteo Perotto ρ, Paolo Bosetti Ѡ, Daniele Fontanelli ¥,
Luigi Palopoli §, Fabiano Zenatti χ, Matteo Zanetti ν, Luca Maule Ѳ& Mattia Tavernini ζ
Author α: PhD Student, Department of Mechanical Engineering,
University of Trento, Trento, Italy. e-mail: alessandro.luchetti@unitn.it
Author σ: Professor, Department of Mechanical Engineering,University
of Trento, Trento, Italy. e-mail: mariolino.dececco@unitn.it
Author ρ: Graduate Student, Department of Mechanical Engineering,
University of Trento, Trento, Italy.
e-mail: matteo.perotto@alumni.unitn.it
Author Ѡ: Professor, Department of Mechanical Engineering,University
of Trento, Trento, Italy. e-mail: paolo.bosetti@unitn.it
Author ¥:Professor, Department of Information Engineering and
Computer Science,University of Trento, Trento, Italy.
e-mail: daniele.fontanelli@unitn.it
Author §:Professor, Department of Information Engineering and
Computer Science,University of Trento, Trento, Italy.
e-mail: luigi.palopoli@unitn.it
Author χ: Engineer, Dolomiti Robotics Srl,Trento, Italy.
e-mail: fabiano.zenatti@dolomitirobotics.it
Author ν: Postdoctoral researcher, Department of Mechanical
Engineering, University of Trento, Trento, Italy.
e-mail: matteo.zanetti@unitn.it
Author Ѳ: Engineer, Postdoc Robosense Srl,Trento, Italy.
e-mail: l.maule@robosense.it
Author ζ: Engineer, Postdoc Robosense Srl,Trento, Italy.
e-mail: m.tavernini@robosense.it
time the inclination of the object and synthesising an
appropriate control strategy based on planned actions.
II. Related Works
An example of a passive system is given by
[12], where each jaw has a vertical V-groove cavity with
a small hard contact point attached to an elastic strip
that orthogonally crosses the groove. When the gripping
force is low, the rotation is obtained by pivoting the
object around the axis created by hard contact points
that are free to rotate. When the force increases, the
strip goes into the groove, thus constraining the object.
The interesting feature is that the type of contact
between the object and the jaw is a function of the
gripping force. Although not suitable for cubic objects,
this solution can be retrofitted to different parallel jaws.
Another possible detrimental effect is that the point-like
contact may damage the object surface, which is also
subject to wear, and requires high accuracy in sensing
the object to grasp and in planning for the proper
gripping point. Additionally, the proper grip ping force is
another feature to be defined, which requires precise
knowledge of the gripper-object friction coefficient.
Two other interesting examples are available in
the literature, both based on pneumatic actuation. The
one presented in [13] solves the problem of the correct
gripping force choice by introducing an active rubber
diaphragm between the jaw body and the fingertip. A
bearing allows the fingertip to freely rotate when the
diaphragm is not inflated, then the inflation allows it to
stop quickly at a given angle. This design has the
advantages of being fast and independent on the object
geometry and grasping force. Additionally, it is
equipped with a rotary magnetic encoder that allows for
feedback control. The main limitation is the need for a
pneumatic system.
The other solution presented in [14] uses an
inflatable membrane to change the shape of the contact
interface: when the pressure is high, the fingers have a
prismatic shape and contact is restricted to a small area
(ideally two points); when the pressure is low, the shape
smoothly becomes a V-groove cavity, where cylindrical
objects are held. The advantage of this design is that it
is independent of the object geometry although it is only
suitable to align cylindrical shapes and, again, it needs
to be actuated by a pneumatic system.
The solution here presented is purely
mechanical and passive, and integrates a sensing
system. It can work with a wide range of object shapes
while avoiding the use of a pneumatic system. It allows
for a simpler, more reliable, and more cost-effective jaw
design. The encoder also performs quick fault
diagnosis, increasing robustness.
III. The Passive-Sensing Jaws
The jaws of the parallel gripper were designed
with an innovative passive auto-alignment capability.
Each of the two jaws has a different design and
accomplishes different functions in the alignment
operation. The one in Fig. 1 only works as a pivot to
align the object, the other in Fig. 2 has three additional
features:
1. a v-groove cavity;
2. a counterweight at an offset to the rotation axis;
3. a rotation sensor.
The v-shape, provided with an elastic film, is
made to better secure cylindrical objects, the
surrounding planar surface is instead used to improve
contact with prismatic pack
Fig. 1: Detail of the jaw provided of polymeric disc pad
Fig. 2: Detail of the jaw provided of balancing mass and
magnetic encoder
ages. The counterweight, on the other hand, has
different functions: when the object is not grasped it
makes the v shaped cavity parallel to the ground,
allowing reliable grasping of cylindrical objects that lie
with their axis parallel to the ground. The direction of the
object’s rotation depends on which side of the center of
gravity it is grasped. On one side of the center of gravity,
Fig. 3 (Extension 1), the object is not aligned because
the rotation imposed by the gravity makes the
counterweight motion in the direction of its end-stroke
already reached. On the other side, Fig. 4 (Extension 1),
the counterweight rotates in the other direction, making
the package free to rotate in the same direction and
reach its vertical position determined with the contact
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with a stop screw. The reason why screws are used as
mechanical stoppers is that the final angle can be tuned
by tightening them. The tuning interval is [-6,20] degrees
around vertical position. The rotation of the contact
surfaces is obtained for both jaws using SKF 628/6-2Z
deep groove ball bearings. The material used for both
jaws is aluminum 7075 with an interchange able
polyoxymethylene pad in the free rotating gripper disc to
change the friction value.
The second jaw, Fig. 2, has a support for the
reading head of a magnetic encoder used to read the
angle value. The sensor used is the LIKA SMB5
magnetic sensor together with LIKA MT50 tape. The
angular resolution after wrap
Fig. 3: Main sequence for moving a cylindrical object
without alignment: (A) gripping with v-shaped cavity
parallel to the ground, (B) lifting with fixed object, (C)
releasing without movement of the jaw disks
Fig. 4: Main sequence for moving a cylindrical object
with alignment: (A-B) gripping with v-shaped cavity
parallel to the ground, (C) lifting with the object rotation
until the end of the counterweight stroke (D), (E-F)
releasing with the counterweight and v-shaped cavity
moving parallel to the ground
ping the magnetic strip on a 32 mm diameter cylinder
be comes 1.15°, maximum speed is 16 m s−1(100 rad
s−1). The encoder is equipped with the external LIKA
IF40 converter that performs interpolation and provides
digital output. The angle sensor is added also with the
purpose of making the system able to detect faults and
misalignment. It enables the robot controller to move the
end-effector so that the object can be aligned in the
best possible way, in terms of time and final angle with
respect to the vertical direction, before re leasing the
package. In case of fault the system could drop the
object and run again an alignment process.
IV. Experimental Analysis on
Pharmaceutical Packaging
The application fields of the developed system
are many. In this work it has been tested for the
alignment of packages for pharmaceutical use. In
particular, their shape can be both prismatic and
cylindrical with the geometric requirements of Fig. 5. As
shown in Fig. 5, the average percentage (AVG) of
cylindrical packages out of the overall worldwide
packages depends on the country. Their weight is less
than 800 g.
The packages has to be placed in a position of
maximum-stability: the cylindrical have to be placed in
vertical position; the prismatic maximizing the contact
surface.
V. System Design
This section describes the important features of
the hard ware components and the developed software
used for statistical experiments.
a) Hardware Components
Fig. 6 shows the components used for testing.
Robot: The robot is the HS-4345 4-axis SCARA robot
designed by Denso robotics. It has four links connected
with three revolute and one prismatic joints.
Controller: The RC8 controller is the interface between
the robot and the PC. From a software point-of-view, the
ORiN middleware is used to build the client application
to communicate with the controller. In this work the
coded client application requests a service sending a
packet over TCP stream using b-CAP communication
protocol. Server assigns commands and responds to
the client to confirm the service execution is completed.
Laser: Keyence LK-G157 laser displacement was used
to set the position of the object’s center of mass with
respect to the object’s main axis. The repeatability of the
instrument is 0.5 µm.
Parallel gripper: The Shunk WSG 50 parallel gripper is
used to actuate prototype jaws. It is equipped with force
and position sensors and controlled sending commands
via TCP/IP protocol.
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b) Software Description
A state machine was designed to control the
whole alignment process, whose main states,
summarised in Fig. 7, are:
Idle: In Idle state the system is waiting to receive the
object information about its shape, dimensions, mass,
position and orientation, and if it has to be placed in
maximum stability condition or only its location has to be
changed. For the test application presented in this
paper, all these information in were provided manually
as input, while, in actual operative conditions, a
dedicated vision system connected to a database will
be used.
Positioning: In positioning state, the robot moves to the
gripping point specified with a final end-effector position
in the operational space and following an optimal
planned path.
Gripping: Here the gripper grabs the object and uses its
force sensors to detect the presence of the object: in
case the object is lost, an error is returned and the
system goes back to Idle state.
Lifting: The robot lifts the object and, depending on the
gripping point, the object will be aligned or not. The
height at which the object is lifted depends on object
dimension and on the gripping point. When package
alignment is needed, the lifting and pivoting phases
show a dumped second order dynamic with different
characteristics for different medicine packages, as
shown by the encoder signals time evolution in Fig. 8
obtained with a sampling frequency of 100 Hz.
Robot Moving: The robot moves the object to the
release point. During this phase the gripper, with its
alignment axis, must always be orthogonal to the
tangent of the trajectory. The direction of the robot’s
motion is selected such that the inertial force caused by
the robot’s acceleration adds an alignment torque to the
package (i.e. it pushes the object to the mechanical
stroke limit). In this way, if the object is already at the
end of its stroke, the lateral acceleration acts on a
constrained degree of freedom and does not affect the
final angle.
Object Release: This final phase is crucial for the
success of the orientation process and only depends on
the final angle at the end of the motion phase. Fig. 9
shows the geometric condition for correctly releasing the
cylinder in its stable configuration (cone stability).
The critical value of β, βcr, is found making the
ratio between the position of the object center of mass
(CM) and the object diameter at the bottom surface.
Perception Loop: In the Perception loop the smart end
effector perceives and combines information such as
the current gripper position P0, the final desired one Pp,
the current object angle (θt), the gripping distance (h)
w.r.t. the center of mass (CM), the object diameter (D)
and its height (H), to plan the trajectory. Those
information are used to realign the package using a
reference alignment vertical plate if the object needs to
be further aligned before release.
In particular, by the perceived information, the
robot moves the gripper to the plate at a distance based
on the radius of the cylindrical envelope of the package
increased by a safety factor (df). A parametric arc
movement forces the gripper to be parallel to the vertical
plate, Fig. 10. The path chosen in this way guarantees
the packages to be tangential to the wall at the final
point Pp.
In this phase, the value of the object inclination
(θt) is constantly checked along the planned gripper
trajectory to determine when the object can be correctly
released.
In Fig. 11 it is shown the sampled signal (100
Hz sampling frequency) of the rotary sensor during a full
Fig. 5: Size of drug packages worldwide
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Fig. 6: System Architecture
alignment cycle and the effect of the different phases of
the alignment cycle on the angle, described in the
Perception Loop phase. In particular, at 3.5 s the object
is gripped and lifted, reaching a final angle of
approximately 40°. The robot forward acceleration
makes the angle to stabilize around 55° at 5.5s, but this
is not sufficient for having a successful release because
the critical angle for this particular package equals 68°.
As a consequence, the robot moves towards the vertical
plate, that makes the final angle to be around 90
degrees allowing a safe release.
The code to manage the finite state machine for
picking, positioning, aligning and releasing operations
is written in C language on Microsoft Windows operating
system. A multi-thread application was coded to
simultaneously control the SCARA robot, the gripper
and read the angle value, Fig. 12.
VI. Design of the Experiments
In this section we carry out a statistical analysis
to validate the system design and present the obtained
results. The design validation should verify the following
hypothesis: system is able to pick up, perform pivoting
and aligning of packages of different shapes and
weights, in a reduced amount of time and with a low
error percentage.
A first factorial screening experiment is
performed in order to identify factors that have stronger
influence on the pivoting capability. The response
surface is then obtained and used to find the factors
combination that leads to the worst final angle and the
largest settling time. In other terms, this
first experiment allows to obtain the worst operating
condition for pivoting success. In this condition the
complete pick align-place operation is performed to
check the robustness of the system with real drug
packages. In this final study, the effectiveness of the
system is assessed through the percentage of
successfully completed alignment operations.
All the statistical analysis was performed in
RStudio, an integrated development environment for R
programming language.
Fig. 7: State Machine with transition conditions
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Fig. 8: Three signals sampled during the lifting and
pivoting phase for different packages: The upper plot
refers to large diameter, high package filled with
homogeneous material. The middle one refers to large
diameter, low height package filled with homogeneous
material and the bottom one refers to small diameter,
high package filled with non-homogeneous material
Fig. 9:
Geometric condition for alignment success (cone
of stability) with β
the package’s angle with the vertical
plane and βcr the threshold angle for stability. In red the
final stability side of the package after release
Fig. 10: Parametric arc curve from initial object’s position
P0 to its final position Pp
a) Screening Experiment
The factorial experiment is the most efficient
type of experiment for screening. After obtaining the
factors significance, the objective is to obtain the
response surface. The factorial design is augmented
with several observations at the center to fit a model
linear in all factors but one, which is quadratic. If the
ANOVA shows that the quadratic term is significative,
then we need to augment the factorial plan to a 3n
Central Composite Design, if not, a linear 2n model is a
reliable approximation. Blocking is used to perform
sequential experimentation and augment the factorial
design only if the second-order model is needed [15].
The choice of factors levels comes from prior
and actual knowledge of the process and is made to fit
the real operating conditions of the process when
performing the central composite design. The design
factors chosen for the factorial experiment with their low
(L), center (C) and high (H) levels are reported below.
Fig. 11: Angle signal sampled during a full alignment
cycle
Fig. 12: Communication protocols overview
A. Distance percentage w.r.t. the geometrical center of
mass, Fig. 13(b); L: 10% - C: 50% - H: 90%.
B. Object diameter; L: 35mm - C: 52.5mm - H: 70mm. C.
Object height; L: 60mm - C: 90mm - H: 120mm.
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D. Distance percentage of the inner material center of
mass w.r.t. the container center of mass, Fig. 13(a)); L:
0% - C : 50% - H : 100%.
E. Robot vertical acceleration (percentage of the
maximum allowed for the package mass); L: 20% - C:
40% - H: 60%.
F. Friction coefficient at gripping interface; L: standard
interface - C: elastic film on V-shaped cavity - H: elastic
film on both interfaces.
G. Gripping force; L: 10N - C: 19N - H: 28N.
All the other controllable factors affecting the
alignment operation are held constant. The most
relevant assumptions made during factors selection are
presented hereafter.
Assuming that the gripper always makes the
pivoting axis orthogonal to the cylinders longitudinal
axis, the only gripper degree of freedom that is varied is
the position along grip ping axis, all the others are held
constant. Also the vertical distance of the grip from the
plane is assumed fixed because, even if there is an error
in estimating the diameter of the object, the V shape
helps to center the grip.
When gripping an object, the gripping force
rises from zero to its nominal value with a dynamics that
depends on equivalent stiffness and damping at
gripping interface. Here the transient is considered
negligible and the force is assumed to ideally go from
zero to its nominal value before gripping starts.
Mass, volume, inertia moment, diameter, height
and gripping distance are not independent factors, so it
is not possible to design an experiment taking all of
them as factors.
Volume V, inertia moment, gripping distance d
and material type are substituted with two factors: the
relative distance of gripping point from the geometrical
center of mass (A) and the relative distance of the center
of mass of the inner material from the geometrical center
of mass of the container (D). This is non zero when the
material is non-homogeneous, while it is zero otherwise.
For non-homogeneous materials, we refer to the
unconstrained material contained within the package,
e.g., pills or powders. On the other hand, for
homogeneous material we refer to uniformly constrained
materials such as fully filled liquid jars or thick creams.
The response variables of particular interest to
characterize the alignment process are the final angle
and the settling time. The first quantifies the alignment in
steady state condition, when oscillations are completely
damped, the second instead takes into account the
alignment dynamics.
To satisfy the statistical requirements of the
independence of observations, the matrix for the final
design was generated randomizing the experiment
order. A set of different 3D printed cylindrical objects is
used to create all combinations of geometrical factors,
shown in Fig. 14. They are filled with materials of
different densities in order to obtain the same mass
value. In Fig. 14, the orange and the black objects are
filled with homogeneous and non-homogeneous
material, respectively. In the latter case, the ratio
between the position of the material center of mass w.r.t.
the cylinder and the cylinder height is constant.
i. Results
The analysis of variance is performed on the
factorial design added with central points. The fitted
model for angle response variable is Angle
∼
A
∗
B
∗
C
∗
D
∗
E
∗
F
∗
G + A2, the one for time variable is Time
∼
A
∗
B
∗
C
∗
D
∗
E
∗
F
∗
G+ A2. The F values and p-values of
the factors are reported in Tab. 1 and Tab. 2. The
quadratic term is added to check if the 2n factorial plan
has to be augmented to a 3n Central Composite Design.
The high p-value of the quadratic term in both models
proves that the linear model is sufficient to describe the
system behaviour.
The analysis of variance response surfaces are
then obtained fitting a first order model to the factorial
data added to central points. Finally, the steepest
descent path is determined to obtain the combination of
factors that led to the worst condition for final angle and
aligning time. A visual interpretation is given here
reporting the values of factors when moving down the
steepest descent path at 0.5 distance from the center
point and at the factors high level (distance 1 from the
center point). Results are reported in Tab. 3 and Tab. 4.
(a) Factor A measurement
(b) Factor D measurement
Fig. 13: Relative definition for factors
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Fig. 14: Cylindrical 3D printed objects representing
different factors combinations
The worst case for the final angle in the chosen
range of operating conditions occurs when pivoting is
performed with:
A: low gripping distance percentage
B: high diameter
Table 1: ANOVA results for angle variable
Factor F value p-value
(A) Gripping distance 341.46 4.84e-13
(B) Diameter
1448.04
< 2.2e-16
(C) Height 996.87 <2.2e-16
(D) Material 37.57
9.521e-09
(E) Vertical acceleration 5.547 0.02
(F) Friction coefficient 14.14 2.54e-4
(G) Gripping force 0.38 0.54
A2 0.60 0.44
Table 2: ANOVA results for time variable
Factor F value p-value
(A) Gripping distance
3157.99
< 2.2e-16
(B) Diameter 280.32 < 2.2e-16
(C) Height 232.70 < 2.2e-16
(D) Material 15.73 1.193-4
(E) Vertical acceleration 11.31 1e-3
(F) Friction coefficient 10.71 1.36e-3
(G) Gripping force 142.88 < 2.2e-16
A2 2.43 0.12
Table 3: Factors value on the steepest descent path for
angle surface
Dist.
A
B
C
D
E
F
G
0
0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.5
-0.20 0.33 -0.29 0.12 -0.02 0.02 0.01
1
-0.42 0.59 -0.60 0.32 -0.09 0.04 0.02
The worst case for the aligning time in the
chosen range of operating conditions occurs when
pivoting is performed with:
A: high gripping distance percentage
B: high diameter
C: high height
D: non-constrained material
E: high vertical acceleration
F: low friction coefficient
G: low gripping force
Table 4: Factors value on the steepest descent path for
time surface
Dist.
A
B
C
D
E
F
G
0
0.00 0.00 0.00 0.00 0.00 0.00 0.00
0.5 0.23 0.28 0.27 -0.09 0.00 -0.06 -0.19
1
0.47 0.55 0.54 -0.21 0.05 -0.13 -0.35
These results are in accordance with the
physics of the problem. The system can be modeled as
a damped physical pendulum with additional energy
loss caused by the bump against the stop screw. The
amount of energy dissipated during the bump depends
on the restitution factor. For what concerns the final
angle, its value depends on the final balance between
the torque of the gravity force that acts on the package
center of mass and the unbalancing mass one. A short
gripping distance implies a short lever arm for the
gravity torque, resulting in a lower angle value. The
unconstrained material moves to the bottom of the
Fig. 15:
Worst package for release (a) and for pivoting
(b)
Global Journal of Researches in Engineering
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Passive Sensing Jaw for Grasping and Orienting
(a) Package 1: 520x44 mm 52 gr (b) Package 2: 90x35 mm 175 gr
container, making the aligning torque after transient to
be larger than the constrained case. It follows that the
worst case is represented by the constrained material
case. A high gripping force can prevent the bearings
from correctly rotate and a low acceleration does not
help in the aligning operation.
Analyzing the time variable, the high gripping
distance con tribute to have a larger torque, while the
non-constrained material adds an aligning torque to the
system when the package starts to rotate. The large
diameter and large height condition makes the inertia
moment, and consequently the kinetic energy, to be
higher. The kinetic energy is also increased by the large
vertical acceleration. Since a larger kinetic energy
implies to have more bumps and a longer transient, the
final aligning time results to be longer.
The time needed for the entire aligning
operation depends on several factors, one of the most
relevant being the package initial and final position and
orientation. Moreover, the aligning angle and time are
highly depending on the robot lateral dynamics. By
assuming that the robot will be operated to minimize the
time of the process, next experiments to validate the
gripper in the whole robotic system are performed fixing
only the geometric conditions at their worst for the
aligning angle:
B: larger diameter
C: short height
D: homogeneous material
b) Testing: Pick-align-place
The worst-case drug package characteristics
found for pivoting in Section 5.1 are not the same as
those for the re lease operation. The latter case is
influenced only by pure geometric considerations from
the values of the position of the center of mass and the
diameter of the object at the bottom surface as seen in
section 4.1. In contrast with the diameter, the position of
the center of mass is affected by uncertainty, especially
if the material contained in the package is not
homogeneous. We can assume that the center of mass
in the geometric center of gravity is a good
approximation and conservative. In fact, even though
pharmaceutical packages contain heterogeneous
material, due to the effect of gravity during rotations, the
inside material would go to the lower part and this would
lower the center of mass increasing the critical angle for
stability. To have a small height and small diameter
implies a lower critical angle, so another package of
drugs was selected to take into account the worst case
for release, which is reported in Fig. 15(b).
In order to provide a complete analysis of the
system, two test campaigns were carried out to validate
the robustness of final design on both worst cases with
packages of Fig. 15. The centres of mass of both
packages correspond to the geometric centres due to
the homogeneity of the material contained inside.
In order to take into account the most critical
source of uncertainty related to the identification of the
object position and therefore its center of mass
estimation, both the previously defined drug packages
are tested randomly varying the gripping point between
10% and 90% of their half eighth (Fig. 16). In both cases,
200 gripping positions are generated from a uniform
distribution, and the releasing success is tested for both
packages after pivoting only and also with the robot
moving on a trajectory.
i. Results
The results of these experiments are presented
in Tab. 5 and Tab. 6. All the incorrect alignments occurs
when the gripping distance is near 10%.
VII. Conclusion
In this paper, we have presented an innovative
design for a smart robotic gripper able to grasp objects
of cylindrical and prismatic shapes and then orient them
through a mechanically passive alignment system. The
gripper is endowed with sensors to detect object
misalignment and, if necessary, uses an external vertical
plate for re-orientation. The statistical performance
analysis shows that the worst condition for the pivoting
operation is represented by small height, large diameter
packages filled with homogeneous material. Moreover,
geometrical considerations on object stability are made
to find the worst packages characteristics for release
success: small height, small diameter, filled with
homogeneous material.
Fig. 16: Gripping interval between 10% and 90% of the
cylindrical package half height
Table 5: Percentage of alignment success for package 1
Table 6:
Percentage of alignment success for package 2
Global Journal of Researches in Engineering
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Passive Sensing Jaw for Grasping and Orienting
Success rate for the worst package for pivoting
After pivoting After motion After aligning loop
84.5 % 97.5 % 98.5 %
Success rate for the worst package for release
After pivoting After motion After aligning loop
87.5 % 96.5 % 99 %
Two commercial packages representing the two
worst cases are tested in different working conditions.
When both packages are released after the vertical
motion of the robot, considering also the robot lateral
motion and using an additional vertical plate as backup
solution to complete the alignment operation, the
success rate considering also the worst cases is around
99%.
The cases in which the alignment is not
successful are those in which the gripping point is close
to the center of gravity. The probability of success
consistently increase with a vision system that can
reliably estimate the position of the centre of mass. The
results shows that the percentage of success of the
system is high even in the worst operating conditions,
see Extension 1.
Acknowledgements
The authors thank GPI SpA 1 for providing the
main requirements and hardware used for testing.
Further thanks go to Lika2
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2 https://www.lika.it
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