Page 184 - Designing Autonomous Mobile Robots : Inside the Mindo f an Intellegent Machine

Hard Navigation vs. Fuzzy Navigation

With a little thought, we realize that we can’t reduce an axis uncertainty to zero. As
the uncertainty becomes very small, only the tiniest implied corrections will yield a
nonzero quality factor (Equation 11.9 and 11.10). In reality, the robot’s uncertainty
is never zero, and the uncertainty estimate should reflect this fact. If a zero uncer-
tainty were to be entered into the quality equation, then the denominator of the
equation would be zero and a divide-by-zero error would result.
For these reasons we should establish a blackboard variable that specifies the mini-
mum uncertainty level for each axis (separately). By placing these variables in a
blackboard, we can tinker with them as we tune the system. At least as importantly,
we can change the factors on the fly. There are several reasons we might want to do this.
The environment itself sometimes has an uncertainty; take for instance a cube farm.
The walls are not as precise as permanent building walls, and restless denizens of the
farm may push at these confines, causing them to move slightly from day to day.
When navigating from cubical walls, we may want to increase the minimum azimuth
and lateral uncertainty limits to reflect this fact.
How much we reduce the uncertainty as the result of a correction is part of the art of
fuzzy navigation. Some of the rules for decreasing uncertainty are:
1. After a correction, uncertainty must not be reduced below the magnitude of
the correction. If the correction was a mistake, the next cycle must be ca-
pable of correcting it (at least partially).

2. After a correction, uncertainty must never be reduced below the value of the
untaken implied correction (the full implied correction minus the correction
taken). This is the amount of error calculated to be remaining.

Uncertainty may be manipulated in other ways, and these will be discussed in up-
coming chapters. It should be apparent that we have selected the simple example of
column navigation in order to present the principles involved. In reality, features
may be treated discretely, or they may be extracted from onboard maps by the robot.
In any event, the principles of fuzzy navigation remain the same. Finally, uncertainty
may be manipulated and used in other ways, and these will be discussed in upcoming
chapters.

167