The visual simulation displays:
• A specific agents traversability or visibility map upon
• A background image unique to that scenario
• Agents moving around the workspace area
• Current agent’s actions
The simulation’s primary goal is to confirm the author’s
claim that implementing the method outlined in Failure An-
ticipation in Pursuit-Evasion will provide the ability to get
assistance to the primary pursuer within the visibility criteria.
This claim is tested using all three scenarios mentioned above.
The secondary goal is to measure the amount of time it takes
to discover potential failures as the time horizon increases. A
regression analysis is performed on the data to determine the
rate of change. The rate of chance is then compared to the
author’s results. The goal is to have the rate of change less
than the O(h3) limit cited by the authors.
Searching problems can be studied using many different
constraints on the problem (Fig. 2). Failure Anticipation in
Pursuit-Evasion mentions about some the constraints the au-
thors decided to use but not all of their decisions. The authors
specified heterogeneous search group, use of a finite discrete
graph and number of targets. This leaves a lot of implemen-
tation details such as target’s motion, pursuer’s motion and
pursuer’s sensory model that are not explicitly stated. The lack
of details on those topics is expected because the algorithm
discussed in the paper does not focus on these items. But,
the lack of these details add uncertainty when attempting to
replicate and evaluate their results.
There are also a few important difference between this
paper’s implementation and the methods described in Failure
Anticipation in Pursuit-Evasion because of a lack o...
... middle of paper ...
...l exploration task. If the high value pursuer could
take target location into account while still exploring the
environment you could greatly reduce the cost of providing
support while increasing the likelihood that the visibility
criteria will always be met.
Sixth, apply D*-lite ideas to the failure prediction algorithm
to get better performance. Between time steps the failure
prediction graph nodes don’t change very much. You could
leverage this by caching and reusing the nodes, similar to
D*-lite, between failure checks to greatly reduce the cost of
predicting potential visibility failure.
 C. Robin and S. Lacroix, Failure anticipation in pursuit-evasion. Proceed-
ings of Robotics: Science and Systems, Sydney, Australia, July, 2012.
 T. Chung, G. Hollinger, and V. Isler., Search and pursuit-evasion in mobile
robotics. Autonomous Robots, 2011.
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