Cooperative multi-robot information acquisition based on distributed robust model predictive control

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 by robust 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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Recommended citation:

@inproceedings{emoto2016cooperative,
  title={Cooperative multi-robot information acquisition based on distributed robust model predictive control},
  author={Emoto, Shuhei and Akkaya, Ilge and Lee, Edward A},
  booktitle={2016 IEEE International Conference on Robotics and Biomimetics (ROBIO)},
  pages={874--879},
  year={2016},
  organization={IEEE}
}