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50 Chapter 3. Search and Rescue as a Weighted Cleaning Problem
Figure 3.12: Boustrophedonic motion across a map. The robot travels along one
entire row and then travels back along the row below, guaranteeing full coverage in
the minimum time
considerable length of time for the Base Map values to entirely approximate zero.
In all other aspects the Perfect and Imperfect Cleaning experiments are identical.
The end condition of this experiment is instead to ‘find’ a certain proportion of
‘people’ from the map. The positions of these people are calculated dynamically,
and people may be found in any of the cells with a known and calculable probability. This probability is based both on the likelihood of there being a person in
that space (taking terms from search theory, the Probability of Area, or POA) and
the likelihood of the robot being able to find them if there is (the Probability of
Detection, or POD). For any cell, therefore, a robot will have a possibility of finding
a person; a robot searching more probable squares is more likely to find a greater
number of people in a set time, however.8 For this experiment the POD (Probability
of Detection) has been taken as being a constant 0.2. In real situations this would
be liable to change depending on location, but for the purposes of the simulation a
constant POD is acceptable.
The different methods are compared at different variable levels to see how long
each takes to find 500 people.
8
It should be noted here that in a real search scenario the goal would be to plan a route optimising
on Probability of Success (or POS, where POS = POA × POD) — the likelihood of finding a person
in a particular cell, rather than the likelihood of the person being in that particular cell.[11] For this
simulation a constant POD is used (i.e. the imperfection of the cleaning is constant irrespective
of location), so the area over which the robot will be passing will be proportionately the same for
POA as for POS.