Nonmyopic Informative Path Planning in Spatio-Temporal Models
by Alexandra Meliou, Andreas Krause, Carlos Guestrin, Joseph M. Hellerstein
Abstract:
In many sensing applications we must continuously gather information to provide a good estimate of the state of the environment at every point in time. A robot may tour an environment, gathering information every hour. In a wireless sensor network, these tours correspond to packets being transmitted. In these settings, we are often faced with resource restrictions, like energy constraints. The users issue queries with certain expectations on the answer quality. Thus, we must optimize the tours to ensure the satisfaction of the user constraints, while at the same time minimize the cost of the query plan. For a single timestep, this optimization problem is NP-hard, but recent approximation algorithms with theoretical guarantees provide good solutions. In this paper, we present a new efficient nonmyopic greedy algorithm, exploiting submodularity of the information collected, that efficiently plans data collection tours for an entire (finite) horizon. Our algorithm can use any single step procedure as a black box, and, based on its properties, provides strong theoretical guarantees for the solution. We also provide an extensive empirical analysis demonstrating the benefits of nonmyopic planning in a real world sensing application.
Citation:
Alexandra Meliou, Andreas Krause, Carlos Guestrin, and Joseph M. Hellerstein, Nonmyopic Informative Path Planning in Spatio-Temporal Models, in Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI), 2007, pp. 602–607.
Bibtex:
@inproceedings{DBLP:conf/aaai/MeliouKGH07,
    Abstract = {In many sensing applications we must continuously gather
    information to provide a good estimate of the state of the environment at
    every point in time. A robot may tour an environment, gathering
    information every hour. In a wireless sensor network, these tours
    correspond to packets being transmitted. In these settings, we are often
    faced with resource restrictions, like energy constraints. The users issue
    queries with certain expectations on the answer quality. Thus, we must
    optimize the tours to ensure the satisfaction of the user constraints,
    while at the same time minimize the cost of the query plan. For a single
    timestep, this optimization problem is NP-hard, but recent approximation
    algorithms with theoretical guarantees provide good solutions. In this
    paper, we present a new efficient nonmyopic greedy algorithm, exploiting
    submodularity of the information collected, that efficiently plans data
    collection tours for an entire (finite) horizon. Our algorithm can use any
    single step procedure as a black box, and, based on its properties,
    provides strong theoretical guarantees for the solution. We also provide
    an extensive empirical analysis demonstrating the benefits of nonmyopic
    planning in a real world sensing application.},
    Author = {Alexandra Meliou and Andreas Krause and Carlos Guestrin and Joseph M. Hellerstein},
    Booktitle = {Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI)},
    Pages = {602-607},
    Title = {\href{http://people.cs.umass.edu/ameli/papers/AAAI2007.pdf}{Nonmyopic Informative Path Planning in Spatio-Temporal Models}},
    Venue = {AAAI},
    address = {Vancouver, Canada},
    month = {July},
    Year = {2007}
}