Instrumentation & Measurement Magazine 24-5 - 33

As said, the force applied to the sensor causes a deformation
of the pad and, in turn, a change in the measured tactile
map. Moreover, the use of a non-axisymmetric shape for the
deformable layer implies, in the presence of torsional moments,
the generation of the torsional warping effect, which
is measurable by the tactile map and guarantees the estimation
of the applied torques. Here, a summary of the procedure,
to invert this relationship and estimate the applied force by
knowing the tactile map, is reported. The mapping is provided
by training an Artificial Neural Network (ANN). The critical
point of this procedure is the training data collection.
The ANN should be able to estimate the contact force and
torque in all of the possible force/torque combinations applied
in all possible points of the sensor pad. The dimensionality of
the problem is large; thus, we need a specific procedure to acquire
the data. The detailed procedure is described in [8]. To
collect the data, the sensor is mounted on a reference force/
torque sensor (Fig. 2). Various forces and torque combinations
are applied on the pad by using a rigid object, and the measured
force and the corresponding tactile map is stored. In [8], a MATLAB
GUI helps the user visualize the data and the calibration
status in order to apply all possible force combinations. The
data are first post-processed to reduce the problem dimensionality
and the sample number, and then they are used to train a
Feed-Forward ANN. The network is made of six hidden layers,
and each hidden layer is made of 90 neurons and a sigmoidal
activation function. Fig. 2 shows an example of force-torque
reconstruction, where the force is reconstructed with a mean error
of 0.2 N and the torque with a mean error of 0.002 Nm.
Robotic Applications
This section reports three possible robotic applications for the
proposed sensing solution. As already stated, the ability to measure
the force applied to the sensor pad can be used in grasping
applications to avoid or control the slippage of a grasped object.
These abilities can be performed by simple parallel jaw
grippers equipped with the sensors. Here, two manipulation
abilities based on the reconstructed forces and torques are described:
the slipping avoidance and the pivoting. The first
consists of firmly grasping an object by applying the " slowest "
grasp force that avoids slippage. The second one consists of letting
the object rotate between the fingers to change the relative
orientation between the gripper and the object. Both abilities
are provided via a model-based approach described in [11].
The contact is modeled via the rototranslational extension of
the Coulomb friction law, i.e., the Limit Surface [12], while the
slipping dynamic is described with a LuGre friction model [13].
The details about slipping avoidance algorithm are described
in [14] and [15]. The control algorithm is represented
in Fig. 3a, comprised of two control actions: a static control action
that provides the minimum grasp force in static condition
by exploiting the Limit Surface theory; and a dynamic action
that computes the grasp force needed when the force variates
rapidly (e.g., when a robot arm lifts an object from a table) and
it is based on a nonlinear slippage observer. Fig. 3b shows an
example, in which the plot reports the measured tangential
August 2021
Fig. 3. Slipping Avoidance. (a) Control Scheme and (b) an experimental
example, illustrating tangential force ft
(blue).
force ft and the grasp force fn
(red line) and actuated grasp force fn
during a lift maneuver. As soon as
the lift begins, the tangential force increases because the sensor
feels the object weight, and at the same time, the slipping
control algorithm automatically computes the normal force
needed to lift the object. The lift was successful without any
noticeable slip. Note that the algorithm can also deal with the
torsional moment, as detailed in the cited papers.
The second example, the pivoting algorithm, is detailed
in [11] and [15]. This maneuver can be executed in two different
modalities called gripper and object pivoting, respectively.
The first one consists of having the object fixed in the space
while the gripper rotates about the grasp axis so as to change
the relative orientation between the gripper and the object.
An example is depicted in Fig. 4a, in which the object has to
be placed in the narrow space between the two shelves on the
right, and the motion is infeasible in the initial configuration
because of collisions with the top shelf. By letting the gripper
rotate with respect to the object, it is possible to reach the new
grasp configuration that makes the grasp feasible.
The second pivoting modality is the dual one and consists
of having the gripper fixed in the space while the object rotates
in a pendulum-like motion. An example is depicted in Fig. 4b,
where the bottle has been grasped, for some reason, in the depicted
horizontal orientation and has to be placed vertically.
The motion is feasible only if the robot has a large workspace
and it is able to place the object from the top. By letting the
IEEE Instrumentation & Measurement Magazine
33

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