Biomedical Engineering Reference
In-Depth Information
safety features. We also address the issue of calibration of IMU to FT sensor.
Beside evaluation of the calibration, we evaluate the FTA sensor's latency and
evaluate the system in realistic worst-case scenarios. We further show that the use
of acceleration recordings is sufficient for gravity compensation for robotized
TMS.
6.1 The FTA Sensor
6.1.1 Combining Acceleration with Force-Torque
We already know that gravity compensation is necessary to subtract the gravity
impact on the tool from the force/torque recordings. So far, we have used the
current
robot
end
effector
pose
R T E
from
the
robot
for
this
compensation
( Sect. 5.1.2 ).
In contrast, an IMU can measure accelerations relative to gravity acceleration.
Hence, the IMU is able to measure the gravity direction in relation to its coordinate
frame. By combining such an IMU with an FT sensor, we can use the accelerations
for gravity compensation. The combination of both sensors will be called FTA
sensor. In contrast to FT sensors, IMUs are available as integrated circuits. As
both, IMU and FT sensor, have their specific coordinate frame, we must perform a
calibration between both sensors. Thereby, we get the transformation matrix
FT T IMU to convert the accelerations A from the IMU to the FT coordinate system:
A FT ¼ FT T IMU A IMU :
ð 6 : 1 Þ
Now, we can use the accelerations to compensate for gravity. We calculate the
expected force F 0
for the current orientation with:
F 0 ¼ A FT f g ;
ð 6 : 2 Þ
where f g is the tool's gravity force corresponding to its weight. We estimate the
applied forces F user and torques M user corresponding to the equations for FT-
control (Eqs. 5.4 and 5.5 ) but with usage of Eq. 6.2 instead of Eq. 5.2 :
F user ¼ F A FT f g ; and
ð 6 : 3 Þ
s ;
M user ¼ M A FT
f g
ð 6 : 4 Þ
with s being the tool's centroid. In this way, robot input is not required for
computing the spatial orientation of the sensor. Hence, it operates independently.

Search WWH ::

Custom Search