Distributed Particle Swarm Optimization - Particle Allocation and Neighborhood Topologies for the Learning of Cooperative Robotic Behaviors

In this article we address the automatic synthesis of controllers for the coordinated movement of multiple mobile robots, as a canonical example of cooperative robotic behavior. We use five distributed noise-resistant variations of Particle Swarm Optimization (PSO) to learn in simulation a set of 50 weights of an artificial neural network. They differ on the way the particles are allocated and evaluated on the robots, and on how the PSO neighborhood is implemented. In addition, we use a centralized approach that allows for benchmarking with the distributed versions. Regardless of the learning approach, each robot measures locally and individually the performance of the group using exclusively on-board resources. Results show that four of the distributed variations obtain similar fitnesses as the centralized version, and are always able to learn. The other distributed variation fails to properly learn on some of the runs, and results in lower fitness when it succeeds. We test systematically the controllers learned in simulation in real robot experiments.