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Multi-robot monitoring in dynamic environments with guaranteed currency of observations | IEEE Conference Publication | IEEE Xplore

Multi-robot monitoring in dynamic environments with guaranteed currency of observations


Abstract:

In this paper we consider the problem of monitoring a known set of stationary features (or locations of interest) in an environment. To observe a feature, a robot must vi...Show More

Abstract:

In this paper we consider the problem of monitoring a known set of stationary features (or locations of interest) in an environment. To observe a feature, a robot must visit its location. Each feature changes over time, and we assume that the currency, or accuracy of an observation decays linearly with time. Thus, robots must repeatedly visit the features to update their observations. Each feature has a known rate of change, and so the frequency of visits to a feature should be proportional to its rate. The goal is to route the robots so as to minimize the maximum change of a feature between observations. We focus on the asymptotic regime of a large number of features distributed according to a probability density function. In this regime we determine a lower bound on the maximum change of a feature between visits, and develop a robot control policy that, with probability one, performs within a factor of two of the optimal. We also provide a single robot lower bound which holds outside of the asymptotic regime, and present a heuristic algorithm motivated by our asymptotic analysis.
Date of Conference: 15-17 December 2010
Date Added to IEEE Xplore: 22 February 2011
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Conference Location: Atlanta, GA, USA

I. Introduction

Consider the following problem. An environment (such as a city, or a building) contains known static features of interest (such as intersections in the city, or rooms in the building). A group of robots is tasked with monitoring the features by visiting their locations. The environment is dynamic, and thus the properties of each feature change over time (i.e., the amount of traffic in each intersection, or the layout and number of people in each room). Features may change on different time scales. Thus, the robots must repeatedly visit the features to update their observations. The frequency of visits to each feature should be proportional to that feature's rate of change. The problem is to determine routes for the robots that allow them to guarantee the currency (or accuracy) of their most recent observations of each feature. That is, to determine routes that minimize the maximum change of a feature between visits (observations). We call this problem persistent monitoring.

References

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