April
2004, Issue 165
Mini
Rover 7
Electronic Compassing fo Mobile Robotics
by
Joseph Miller
SOFT/HARD
IRON DISTORTION
As
you now, producing a compass heading with a pair of
sensors is fairly straightforward. However, there’s
trouble in the neighborhood. Magnetic anomalies are
all around you. Other magnetic sources and alternate
magnetic flux pathways are corrupting your intended
signal—the Earth’s magnetic field.
Magnetic
fields are generated by electrical current (electromagnetism)
in your circuitry and neighboring appliances. There
are permanent magnets in your robot’s motors, and there
is magnetized metal in the robot’s construction materials.
Items
external to the robot, such as appliances, underground
pipes, and ducts may have their own fields, and can
locally offset or change the direction of the Earth’s
field. Items that create offsetting fields are classified
as hard iron. A soft iron material is anything that
alters the natural path of a magnetic field’s lines
of flux. By "natural path" I mean its path through air.
Put another way, any material with a relative permeability
other than one is a soft iron material. A soft iron
also can have a hard iron effect. Usually soft iron
and hard iron materials are referred to by the effect
they have on the surrounding magnetic field. These effects
are easy to recognize when an x-y graph is plotted of
the compass sensors output values as the compass is
rotated through 360°. Figure 3 illustrates these effects.
All
of the plots assume that the culprit material is local,
which is to say that it is onboard the robot when it
is rotated. These traces, or profiles, can be thought
of as the magnetic signatures of the robot’s local environment.
Notice how the soft iron effects create an elliptical
profile. Soft iron material attracts, bends, and condenses
magnetic flux toward itself, creating sparse field densities
on its lateral sides. At the same time, it will also
create dense magnetic fields in areas that are in line
with it and the source field direction, as shown in
Figure 4. The distribution and quantity of soft iron
material will determine the alignment and offset of
the ellipse’s foci.
 |
| Figure
4—Here you can see the effects of soft iron distortions
in the Earth’s magnetic field. |
Nearby
electromagnetic fields, ferrous construction materials,
and motors make robots especially tough environments
for compasses. Before using an electronic compass in
a mobile robot, it is a good idea to analyze its magnetic
signature by creating an x-y sensor plot. From this
plot, you can identify the existence of the aforementioned
simple distortions and, more importantly, the more complex
ones. The plots should give you an indication of the
compass’s chances of providing accurate readings, as
well as information to help you make corrective adjustments
to the robot or sensor placement.
Modern
electronic compasses have little trouble mathematically
correcting for simple soft iron and hard iron distortions,
sensor gain mismatch, and even sensor misalignment (orthogonality),
which are all shown in Figure 3. Electronic compasses
calculate the correctional coefficients after completing
a one-time calibration procedure that involves rotating
the robot or vehicle through at least one complete rotation
so that it can analyze the geometry of the x-y sensor
data.
When
testing your robot and creating plots for your analysis,
the patterns to watch for are excessive hard iron offsets
and multimode soft iron distortions, which look like
lumpy circles or ellipses. Excessive hard iron offsets
can bring sensors into their nonlinear regions. This
looks like a flat spot on the x-y circle plots, which
are perpendicular to the axis of the saturated sensor.
It is also a good idea to create plots for the robot
when it is rotating both clockwise and counterclockwise,
and then compare the two to see if the motor currents
affect sensor readings.
Figure
5 shows x-y sensor plots (magnetic signature plots)
running on my Mini Rover 7 robot. The red trace shows
that the robot has some hard iron offsets but no soft
iron distortions. I rotated the robot in both directions
under its own power and saw nothing significantly different,
although it would be hard to see any rotational offsets
with these plots. There are ways to test this.
|
|
| Figure
5—Here are several magnetic signature plots of the
Mini Rover 7 with additional effects. |
The
slightly elliptical blue trace is the result of placing
a 9-V battery next to the compass. I placed a small
magnet in the robot when I generated the light blue
trace. The dark blue trace is the result of placing
a PC chassis next to the robot when it performed a rotation.
The small amplitude is to be expected because the external
soft iron material attracts the magnetic flux away from
the robot, therefore creating an area with a sparse
field, much like the one in Figure 4. You should not
calibrate the compass in your robot under this condition
because it does not represent a normal condition. Any
electronic compass with hard iron and soft iron compensation
should have no problem producing accurate heading data
using any of the vehicle magnetic signatures shown in
Figure 5.
As
you have seen, with a little effort a compass can adapt
itself to your robot’s unique internal magnetic signature
by using its built-in calibration algorithm. However,
when operating after calibration, your robot will most
likely come across external hard iron and soft iron
objects, which will alter the Earth’s magnetic field
direction.
Figure
4 shows how a soft iron object can alter the heading
of a robot as it attempts to keep steady (north in this
case) when it moves past the object. Notice that the
field density is sparse in this area. As long as the
sensors are relatively close (so that they experience
the same field density ratios), the compass abilities
are not significantly diminished. This is because the
heading calculation for a quadrature sensor pair uses
the arctangent of the ratio of the two sensors (see
Equation 2). The overall field density, or magnitude
of the field density, can be monitored by calculating
the geometric sum of the two sensors (see Equation 4),
which can alert you to such anomalies. This information
can be used to weigh your options.
You
may decide to temporarily trust differential wheel encoder
outputs for tracking heading while the overall measured
field density falls outside established thresholds.
A Kalman filter or your own statistical algorithm can
use this information with other sensor data to make
improved estimates of actual heading. It is important
to use the compass’s calibrated x-y sensor data to compute
field density because it already has been compensated
for your robot’s personal magnetic signature. Modern
electronic compasses like the PNI V2Xe can report the
overall field density in proportion to the field that
it experienced at every heading angle during a calibration
cycle so that local field distortions do not interfere
with the assessment of external magnetic fields.
