Citation
Shuhei Emoto, Ilge Akkaya, Edward A. Lee. "Information Seeking and Model Predictive Control of A Cooperative Multi-Robot System". Artificial Life and Robotics, 21(4):393-398, October 2016.

Abstract
In this paper, we propose a cooperative multi-robot control system, operating in an unfamiliar or unstructured environment. We focus on a robust model predictive control (robust-MPC) framework that enables robotic agents to operate in uncertain environments, and study the effect of observation uncertainties that arise from sensor noise on cooperative control performance. The proposed system relies on cooperative observation based on an information seeking theory, in which the system not only can compensate uncertainty, but also takes actions to mitigate it. We carry out a case study that demonstrates a multi-robot collision avoidance scenario in an unknown environment. Simulation results show that the combination of robust-MPC methods and cooperative observation enables the cooperative multi-robot system to move efficiently and reach the goal faster than an uncooperative scenario.

Electronic downloads

• Artificial_Life_and_Robotics_camready.pdf · application/x-octet-stream · 828 kbytes
• http://dx.doi.org/10.1007/s10015-016-0315-4
• http://rdcu.be/wrf1

• HTML

  Shuhei Emoto, Ilge Akkaya, Edward A. Lee. <a
  href="http://chess.eecs.berkeley.edu/pubs/1178.html"
  >Information Seeking and Model Predictive Control of A
  Cooperative Multi-Robot System</a>,
  <i>Artificial Life and Robotics</i>,
  21(4):393-398, October 2016.

• Plain text

  Shuhei Emoto, Ilge Akkaya, Edward A. Lee. "Information
  Seeking and Model Predictive Control of A Cooperative
  Multi-Robot System". <i>Artificial Life and
  Robotics</i>, 21(4):393-398, October 2016.

• BibTeX

  @article{EmotoAkkayaLee16_InformationSeekingModelPredictiveControlOfCooperative,
      author = {Shuhei Emoto and Ilge Akkaya and Edward A. Lee},
      title = {Information Seeking and Model Predictive Control
                of A Cooperative Multi-Robot System},
      journal = {Artificial Life and Robotics},
      volume = {21},
      number = {4},
      pages = {393-398},
      month = {October},
      year = {2016},
      abstract = {In this paper, we propose a cooperative
                multi-robot control system, operating in an
                unfamiliar or unstructured environment. We focus
                on a robust model predictive control (robust-MPC)
                framework that enables robotic agents to operate
                in uncertain environments, and study the effect of
                observation uncertainties that arise from sensor
                noise on cooperative control performance. The
                proposed system relies on cooperative observation
                based on an information seeking theory, in which
                the system not only can compensate uncertainty,
                but also takes actions to mitigate it. We carry
                out a case study that demonstrates a multi-robot
                collision avoidance scenario in an unknown
                environment. Simulation results show that the
                combination of robust-MPC methods and cooperative
                observation enables the cooperative multi-robot
                system to move efficiently and reach the goal
                faster than an uncooperative scenario.},
      URL = {http://chess.eecs.berkeley.edu/pubs/1178.html}
  }
  

Posted by Mary Stewart on 18 Jul 2016.
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