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.

Electronic downloads

• http://www.src.org/library/publication/p068351/. (SRC prepublication page)
• http://www.georgejpappas.org//papers/0528.pdf

• 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.