Page 110 - Introduction to Autonomous Mobile Robots

Perception

range 95
precision = --------------- (4.4)
σ
Note that only σ and not µ has impact on precision. In contrast, mean error µ is
directly proportional to overall sensor error and inversely proportional to sensor accuracy.

4.1.2.3 Characterizing error: the challenges in mobile robotics
Mobile robots depend heavily on exteroceptive sensors. Many of these sensors concentrate
on a central task for the robot: acquiring information on objects in the robot’s immediate
vicinity so that it may interpret the state of its surroundings. Of course, these “objects” sur-
rounding the robot are all detected from the viewpoint of its local reference frame. Since
the systems we study are mobile, their ever-changing position and their motion have a sig-
nificant impact on overall sensor behavior. In this section, empowered with the terminol-
ogy of the earlier discussions, we describe how dramatically the sensor error of a mobile
robot disagrees with the ideal picture drawn in the previous section.
Blurring of systematic and random errors. Active ranging sensors tend to have failure
modes that are triggered largely by specific relative positions of the sensor and environment
targets. For example, a sonar sensor will produce specular reflections, producing grossly
inaccurate measurements of range, at specific angles to a smooth sheetrock wall. During
motion of the robot, such relative angles occur at stochastic intervals. This is especially true
in a mobile robot outfitted with a ring of multiple sonars. The chances of one sonar entering
this error mode during robot motion is high. From the perspective of the moving robot, the
sonar measurement error is a random error in this case. Yet, if the robot were to stop,
becoming motionless, then a very different error modality is possible. If the robot’s static
position causes a particular sonar to fail in this manner, the sonar will fail consistently and
will tend to return precisely the same (and incorrect!) reading time after time. Once the
robot is motionless, the error appears to be systematic and of high precision.
The fundamental mechanism at work here is the cross-sensitivity of mobile robot sen-
sors to robot pose and robot-environment dynamics. The models for such cross-sensitivity
are not, in an underlying sense, truly random. However, these physical interrelationships
are rarely modeled and therefore, from the point of view of an incomplete model, the errors
appear random during motion and systematic when the robot is at rest.
Sonar is not the only sensor subject to this blurring of systematic and random error
modality. Visual interpretation through the use of a CCD camera is also highly susceptible
to robot motion and position because of camera dependence on lighting changes, lighting
specularity (e.g., glare), and reflections. The important point is to realize that, while sys-
tematic error and random error are well-defined in a controlled setting, the mobile robot can
exhibit error characteristics that bridge the gap between deterministic and stochastic error
mechanisms.