Coordinating the adaptive behavior for swarm robotic systems by using topology and weight evolving artificial neural networks

Abstract

Swarm robotics (SR) is the research field of multirobot systems, which consist of many homogeneous autonomous robots without any types of global controllers. Generally, since a task given to this system cannot be achieved by a single robot, cooperative behavior is expected to emerge in a robotic swarm by a certain mechanism, which is through the interactions among robots or with an environment. In this paper, an evolutionary robotics approach, in which robot controllers are designed by evolving artificial neural networks, is adopted. Among the many approaches to evolving artificial neural networks, two approaches, NEAT and MBEANN are adopted for conducting computer simulations. Although a conventional neural network has a fixed topology and evolves only with its synaptic weights, NEAT and MBEANN evolve not only with their synaptic weights, but also with their topologies. As a benchmark for swarm robotics, cooperative package-pushing problems using ten autonomous robots are conducted to evaluate their performance. The behavioral characteristics that emerge are then discussed.