Hi and welcome to my presentation on
optimal control for electromagnetic haptic
guidance systems.
Pen-based interactive systems that
feature haptic guidance,
support users in a variety of
applications such as drawing
sketching, writing or CAD design.
Crucially, such systems aim to strike a
balance between giving users a strong
sense of control
and agency. Previous magnet-based work
such as the dePENd
relies on open loop control or like KATIB
time-dependent
closed-loop control. these
strategies
force users to adapt to the system
rather than the other way around.
This gives rise to the question on how
we can control haptic feedback systems,
especially electromagnet-based, to find a
balance between haptic feedback
and user autonomy. In our paper we make
two distinct contributions.
Our first contribution is a time
independent closed-loop control
strategy
which enables user autonomy. The second
contribution
is a prototypical hardware
implementation. Let's first discuss our
control strategy in more detail.
For the control strategy we assume there
is a desired trajectory and a pen
location.
We can discretize the trajectory.
With a discretized trajectory some
questions arise that we want to solve
with our control.
First what is the closest point on the
path.
Secondly what is the desired progress.
These two questions are not new and
answered in previous work.
Albeit in a different context. The final
question is what haptic force needs to
be applied.
These questions are answered combined in
a model predictive contour control
formulation,
which is an iterative optimization
framework.
The third and final question is the main
contribution of our paper.
Therefore let's zoom in on this.
In our case the electromagnet is always
attracting the pen.
This happens from the center of a
permanent magnet embedded in the pen
in a straight line to the center of the
electromagnet.
For the sake of simplicity we will
forget about the z-component
of this force. This force is mainly
depending
on two factors. The distance between the
two magnets
as well as the activation of the
electromagnet. This describes the actual
force.
Calculating the force between two
magnets is not straightforward.
Crucially, this enables real-time control.
For those interested we derive an
analytical equation to do so in our
paper.
The second element of our cluster is the
desired force.
This force is in the direction of the
desired point on the path.
The final cost is the squared difference
between the desired and actual force.
Now that we discussed our proposed
method let's take a look at the hardware
we implemented it on.
The system consists of an electromagnet
mounted on a bi-axial linear stage.
The system is agnostic to the tablet
that is used to determine the pen
location.
This can be pressure based or tablet
with visual feedback.
Furthermore the tool needs to be
augmented with the permanent magnet.
With our proposed method and
prototypical hardware in place lets
evaluate.
We did two experiments. First of all, we
tested our system with end users on
general accuracy,
and secondly with a set of different
users we compared control strategies.
For details of both please refer to the
paper.
Let's discuss the end use performance in
more detail.
We asked 12 participants to draw six
different shapes with and without haptic
feedback.
The users received minimal visual
support during this task.
What we learned from this is that more
complex shapes benefit most from haptic
feedback.
Furthermore, the users are still limited
by the physical speed of our system,
and, finally, the drwaings overall become
more accurate.
Now that we have taken a look at the
evaluations let's discuss potential use
cases.
Ss shown here one of the applications
could be to help users with calligraphy.
A second application could be to help
people with the outlining and inking of
complicated sketches. In this case a
dragon is drawn by a novice user
without drawing experience. Notice how the
system can deal with different line
segments and the lifting of the tool,
as well as swapping of the tool. These
illustrations are drawn with the same
system settings.
This illustrates how the user is still
guided back to the same primitive shapes,
yet remains to have created freedom.
Finally, we show a use case with visual
support, where a user is manipulating
virtual tools
in order to support them with the design
of a chair.
To summarize represented and control
strategy for haptic feedback
prototypical hardware and we've
validated our system on users.
All blueprints and code are available on
our website. Thank you.