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Distributed Learning of Cooperative Robotic Behaviors Using Particle Swarm Optimization

  • Ezequiel Di MarioEmail author
  • Iñaki Navarro
  • Alcherio Martinoli
Chapter
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 109)

Abstract

In this paper we study the automatic synthesis of robotic controllers for the coordinated movement of multiple mobile robots. The algorithm used to learn the controllers is a noise-resistant version of Particle Swarm Optimization, which is applied in two different settings: centralized and distributed learning. In centralized learning, every robot runs the same controller and the performance is evaluated with a global metric. In the distributed learning, robots run different controllers and the performance is evaluated independently on each robot with a local metric. Our results from learning in simulation show that it is possible to learn a cooperative task in a fully distributed way employing a local metric, and we validate the simulations with real robot experiments where the best solutions from distributed and centralized learning achieve similar performances.

Keywords

Particle Swarm Optimization Real Robot Proximity Sensor Cooperative Task Overhead Camera 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This research was supported by the Swiss National Science Foundation through the National Center of Competence in Research Robotics.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Ezequiel Di Mario
    • 1
    Email author
  • Iñaki Navarro
    • 1
  • Alcherio Martinoli
    • 1
  1. 1.Distributed Intelligent Systems and Algorithms LaboratorySchool of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de LausanneLausanneSwitzerland

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