DS-2002-01:
Massios, Nikos
(2002)
*Decision-Theoretic Robotic Surveillance.*
Doctoral thesis, University of Amsterdam.

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## 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.

Item Type: | Thesis (Doctoral) |
---|---|

Report Nr: | DS-2002-01 |

Series Name: | ILLC Dissertation (DS) Series |

Year: | 2002 |

Subjects: | Computation Logic |

Depositing User: | Dr Marco Vervoort |

Date Deposited: | 14 Jun 2022 15:16 |

Last Modified: | 14 Jun 2022 15:16 |

URI: | https://eprints.illc.uva.nl/id/eprint/2030 |

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