Evolving collective cognition for object identification in foraging robotic swarms

This paper focuses on the collective cognition by robotic swarms. The robotic swarms expected to perform tasks that are beyond the capability of a single robot by collective behavior that emerge from local interactions, similar to biological swarms. However, the robotic swarms have to rely on the collective cognition more than biological swarms when considering the limitation in sensory capabilities and the cost of each robot. In this paper, we develop controllers for a robotic swarm to accomplish a foraging task that requires collective cognition. In this task, robots have to both collectively distinguish two objects, namely food and poison, and cooperatively transport food objects to the nest. We applied an evolutionary robotics approach with the covariance matrix adaptation evolution strategy to develop controllers for robotic swarms. The results of computer simulations show that collective cognition was successfully developed, which allows the robots to transport only food objects. In addition, we also perform experiments to examine the scalability and the flexibility of the developed controllers.