*banner
 

Information Seeking and Model Predictive Control of A Cooperative Multi-Robot System
Shuhei Emoto, Ilge Akkaya, Edward A. Lee

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

Citation formats  
  • 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.
For additional information, see the Publications FAQ or contact webmaster at chess eecs berkeley edu.

Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright.

©2002-2018 Chess