Distributed vs. Centralized Particle Swarm Optimization for Learning Flocking Behaviors

In this paper we address the automatic synthesis of controllers for the coordinated movement of multiple mobile robots. We use a noise-resistant version of Particle Swarm Optimization to learn in simulation a set of 50 weights of a plastic artificial neural network. Two learning strategies are applied: homogeneous centralized learning, in which every robot runs the same controller and the performance is evaluated externally with a global metric, and heterogeneous distributed learning, in which robots run different controllers and the performance is evaluated independently on each robot with a local metric. The two sets of metrics enforce Reynolds’ flocking rules, resulting in a good correspondence between the metrics and the flocking behaviors obtained. Results demonstrate that it is possible to learn the collective task using both learning approaches. The solutions from the centralized learning have higher fitness and lower standard deviation than those learned in a distributed manner. We test the learned controllers in real robot experiments and also show in simulation the performance of the controllers with increasing number of robots.

Andrews, Paul
Caves, Leo
Doursat, René
Hickinbotham, Simon
Polack, Fiona
Stepney, Susan
Taylor, Tim
Timmis, Jon
Published in:
Proceedings of the European Conference on Artificial Life 2015, 302-309
Presented at:
13th European Conference on Artificial Life (ECAL 2015), York, United Kingdom, 20-24 July 2015
The MIT Press

 Record created 2015-07-01, last modified 2018-03-17

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