Information Acquisition with Sensing Robots: Algorithms and Error Bounds
Nikolay A. Atanasov, Jerome Le Ny, Kostas Daniilidis, George Pappas

Citation
Nikolay A. Atanasov, Jerome Le Ny, Kostas Daniilidis, George Pappas. "Information Acquisition with Sensing Robots: Algorithms and Error Bounds". Proc. of the IEEE International Conference on Robotics and Automation (ICRA), 31, May, 2014; Comment: 9 pages (two-column); 2 figures; Manuscript submitted to the 2014 IEEE International Conference on Robotics and Automation (ICRA).

Abstract
Utilizing the capabilities of configurable sensing systems requires addressing difficult information gathering problems. Near-optimal approaches exist for sensing systems without internal states. However, when it comes to optimizing the trajectories of mobile sensors the solutions are often greedy and rarely provide performance guarantees. Notably, under linear Gaussian assumptions, the problem becomes deterministic and can be solved off-line. Approaches based on submodularity have been applied by ignoring the sensor dynamics and greedily selecting informative locations in the environment. This paper presents a non-greedy algorithm with suboptimality guarantees, which does not rely on submodularity and takes the sensor dynamics into account. Our method performs provably better than the widely used greedy one. Coupled with linearization and model predictive control, it can be used to generate adaptive policies for mobile sensors with non-linear sensing models. Applications in gas concentration mapping and target tracking are presented.

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  • HTML
    Nikolay A. Atanasov, Jerome Le Ny, Kostas Daniilidis, George
    Pappas. <a
    href="http://www.terraswarm.org/pubs/126.html"
    >Information Acquisition with Sensing Robots: Algorithms
    and Error Bounds</a>, Proc. of the IEEE International
    Conference on Robotics and Automation (ICRA), 31, May, 2014;
    Comment: 9 pages (two-column); 2 figures; Manuscript
    submitted to the 2014 IEEE International Conference on
    Robotics and Automation (ICRA).
  • Plain text
    Nikolay A. Atanasov, Jerome Le Ny, Kostas Daniilidis, George
    Pappas. "Information Acquisition with Sensing Robots:
    Algorithms and Error Bounds". Proc. of the IEEE
    International Conference on Robotics and Automation (ICRA),
    31, May, 2014; Comment: 9 pages (two-column); 2 figures;
    Manuscript submitted to the 2014 IEEE International
    Conference on Robotics and Automation (ICRA).
  • BibTeX
    @inproceedings{AtanasovLeNyDaniilidisPappas14_InformationAcquisitionWithSensingRobotsAlgorithmsError,
        author = {Nikolay A. Atanasov and Jerome Le Ny and Kostas
                  Daniilidis and George Pappas},
        title = {Information Acquisition with Sensing Robots:
                  Algorithms and Error Bounds},
        booktitle = {Proc. of the IEEE International Conference on
                  Robotics and Automation (ICRA)},
        day = {31},
        month = {May},
        year = {2014},
        note = {Comment: 9 pages (two-column); 2 figures;
                  Manuscript submitted to the 2014 IEEE
                  International Conference on Robotics and
                  Automation (ICRA)},
        abstract = {Utilizing the capabilities of configurable sensing
                  systems requires addressing difficult information
                  gathering problems. Near-optimal approaches exist
                  for sensing systems without internal states.
                  However, when it comes to optimizing the
                  trajectories of mobile sensors the solutions are
                  often greedy and rarely provide performance
                  guarantees. Notably, under linear Gaussian
                  assumptions, the problem becomes deterministic and
                  can be solved off-line. Approaches based on
                  submodularity have been applied by ignoring the
                  sensor dynamics and greedily selecting informative
                  locations in the environment. This paper presents
                  a non-greedy algorithm with suboptimality
                  guarantees, which does not rely on submodularity
                  and takes the sensor dynamics into account. Our
                  method performs provably better than the widely
                  used greedy one. Coupled with linearization and
                  model predictive control, it can be used to
                  generate adaptive policies for mobile sensors with
                  non-linear sensing models. Applications in gas
                  concentration mapping and target tracking are
                  presented. },
        URL = {http://terraswarm.org/pubs/126.html}
    }
    

Posted by Nikolay A. Atanasov on 29 Sep 2013.

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