Decision-Theoretic Robotic Surveillance
Nikos Massios
Abstract:
%Nr: DS-2002-01
%Author: Nikos Massios
%Title: Decision-Theoretic Robotic Surveillance
The subject of this thesis is the investigation of autonomous
surveillance planning for an office-like environment. Surveillance can
be informally defined as ``a close watch kept over something or
someone with the purpose of detecting the occurrence of some relevant
events''. Humans perform surveillance tasks quite well, intergrating
sensing, action, and decision-making flawlessly. Automation of each of
these aspects to enable robotic surveillance is non-trivial. In this
thesis, we focus on the decision-making invloved in ``where to go
next''.
We approach this problem of surveillance planning by viewing it as a
probabilistic decision process, ignoring for now the separate problem
of knowing the probabilities and cost in actual situations. We are
eventually interested in an algorithmic implementation of such a
decision process, so we need to consider aspects of formalisation as
well as of efficient computability.
To simplify the discussion we focus on one type of relevant events.
The events considered are probabilistic, independent of each other,
localised within office rooms and produce some costly damage when
present. We took an idealised version of fire as an example of such an
event.
Surveillance planning is a relatively new field and few quantitative
results are known. For this exploratory research effort, various
representations and solution methods of a decision-theoretic nature
are considered. The problem can be mapped into formalisms like (PO)MDP
or classical decision theory in many seemingly different ways, which
are in fact thought to be equivalent. The formalisation conveys the
exponential nature of surveillance planning viewed as an optimal
search problem. Consequently, this thesis emphasises the computational
issues raised by the desire to compute decisions in reasonable time.
The first option for dealing with the computational issues is to limit
the look-ahead of the search. This is what is typically done in
optimal search problems to control the size of the search
space. However, if a small look-ahead is used, the results generated
are not acceptable because they fall prey to local minima problems: if
a certain area is not important enough to be visited, it may also prevent
other areas beyond it from being explored.
Our solution is to move up from the details and to abstract the
problem. An abstracted representation of a target environment for
surveillance can be constructed by grouping similar locations into
clusters. The decisions then are taken among the various ways in which
the clusters can be visited. Search methods based on abstraction boost
the effective look-ahead but are necessarily approximate. This creates
a hard balancing act between finding a method that is coarse enough to
be computable and fine enough to closely approximate the optimal
solution. Deciding on this dilemma is not easy, but we show that the
structure of the problem can be useful. In our surveillance planning
problem for an office building, the topology and the pattern of costs
of the environment largely guide the actions of the robot and this
should be reflected in appropriate clusterings. It turns out that for
office buildings, a sensible general method can be presented for
grouping locations of similar topological structure into clusters
shaped as stars and corridors.
A new decision strategy for such an abstracted building called the
fixed cluster route strategy is proposed. The fixed cluster route
strategy computes the expected cost for a predefined route within a
cluster instead of giving a heuristic estimate of the cost for all
possible routes within the cluster. Three route types are considered:
explore, transit and ignore. The robot then commits itself to the
predefined route it selects by comparing the expected costs at a fixed
decision-level.
The fixed cluster route strategy is still heuristic, but simulation
results show that it beats other simpler strategies, also presented in
this thesis, in cases where local minima are present. It is believed
that this strategy can be further improved, since it loses from a
simple one-step look-ahead minimisation of time between visits when no
cost structure is present. The main contribution of this thesis is
probably to the theoretical understanding of the surveillance planning
problem. The fixed cluster route strategy suggests that abstraction
may be the route to achieving automated surveillance planning.