Citation
Shuhei Emoto, Ilge Akkaya, Edward A. Lee. "Cooperative Multi-Robot Information Acquisition based on Distributed Robust Model Predictive Control". (to appear in) Proceedings of the 2016 IEEE International Conference on Robotics and Biomimetics, IEEE, 16, December, 2016.

Abstract
In this paper, we propose a distributed multi-robot control system working in dynamic and uncertain environments. Robust model predictive control (robust MPC) enables robots to deal with uncertainties. However, the performance of the robust MPC is dependent on the amount of uncertainty that derives from noisy measurements, communication disturbance, etc. The proposed system includes multiple observation robots that gather information cooperatively as well as a main robot controlled byrobust MPC. Therefore, the system works for not only treating the uncertainty but also decreasing it. A simulation result of a collision avoidance shows that the information acquisition by the observation robots enables the main robot to move efficiently and arrive at the goal faster than a case without the observation robots. We also focus on a problem that a large number of observation robots will increase the frequency of inter-robot collision avoidances, and thus negatively affect to the performance of the main robot. Simulation results under various conditions on a disturbance level and a measurement range of sensors clarifies an adequate number of observation robots as well as the design guideline about sensors and networks.

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• HTML

  Shuhei Emoto, Ilge Akkaya, Edward A. Lee. <a
  href="http://chess.eecs.berkeley.edu/pubs/1180.html"
  >Cooperative Multi-Robot Information Acquisition based on
  Distributed Robust Model Predictive Control</a>, (to
  appear in) <em>Proceedings of the 2016 IEEE
  International Conference on Robotics and
  Biomimetics</em>, IEEE, 16, December, 2016.

• Plain text

  Shuhei Emoto, Ilge Akkaya, Edward A. Lee. "Cooperative
  Multi-Robot Information Acquisition based on Distributed
  Robust Model Predictive Control". (to appear in)
  <em>Proceedings of the 2016 IEEE International
  Conference on Robotics and Biomimetics</em>, IEEE, 16,
  December, 2016.

• BibTeX

  @inproceedings{EmotoAkkayaLee16_CooperativeMultiRobotInformationAcquisitionBasedOnDistributed,
      author = {Shuhei Emoto and Ilge Akkaya and Edward A. Lee},
      title = {Cooperative Multi-Robot Information Acquisition
                based on Distributed Robust Model Predictive
                Control},
      booktitle = {(to appear in) <em>Proceedings of the 2016 IEEE
                International Conference on Robotics and
                Biomimetics</em>},
      organization = {IEEE},
      day = {16},
      month = {December},
      year = {2016},
      abstract = {In this paper, we propose a distributed
                multi-robot control system working in dynamic and
                uncertain environments. Robust model predictive
                control (robust MPC) enables robots to deal with
                uncertainties. However, the performance of the
                robust MPC is dependent on the amount of
                uncertainty that derives from noisy measurements,
                communication disturbance, etc. The proposed
                system includes multiple observation robots that
                gather information cooperatively as well as a main
                robot controlled byrobust MPC. Therefore, the
                system works for not only treating the uncertainty
                but also decreasing it. A simulation result of a
                collision avoidance shows that the information
                acquisition by the observation robots enables the
                main robot to move efficiently and arrive at the
                goal faster than a case without the observation
                robots. We also focus on a problem that a large
                number of observation robots will increase the
                frequency of inter-robot collision avoidances, and
                thus negatively affect to the performance of the
                main robot. Simulation results under various
                conditions on a disturbance level and a
                measurement range of sensors clarifies an adequate
                number of observation robots as well as the design
                guideline about sensors and networks.},
      URL = {http://chess.eecs.berkeley.edu/pubs/1180.html}
  }
  

Posted by Mary Stewart on 20 Sep 2016.
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