We present two empirical studies on the design of control software for robot swarms. In Study A, Vanilla and EvoStick, two previously published automatic design methods, are compared with human designers. The comparison is performed on five swarm robotics tasks that are different from those on which Vanilla and EvoStick have been previously tested. The results show that, under the experimental conditions considered, Vanilla performs better than EvoStick, but it is not able to outperform human designers. The results indicate that Vanilla's weak element is the optimization algorithm employed The main contributors to this research are G. Francesca and M. Birattari. AutoMoDe and Vanilla were conceived and developed by G. Francesca, M. Brambilla, A. Brutschy, V. Trianni, and M. Birattari.123 Swarm Intell to search the space of candidate designs. To improve over Vanilla and with the final goal of obtaining an automatic design method that performs better than human designers, we introduce Chocolate, which differs from Vanilla only in the fact that it adopts a more powerful optimization algorithm. In Study B, we perform an assessment of Chocolate. The results show that, under the experimental conditions considered, Chocolate outperforms both Vanilla and the human designers. Chocolate is the first automatic design method for robot swarms that, at least under specific experimental conditions, is shown to outperform a human designer.

We present a novel technology that allows real robots to perceive an augmented reality environment through virtual sensors. Virtual sensors are a useful and desirable technology for research activities because they allow researchers to quickly and efficiently perform experiments that would otherwise be more expensive, or even impossible. In particular, augmented reality is useful (i) for prototyping and assessing the impact of new sensors before they are physically produced; and (ii) for developing and studying the behaviour of robots that should deal with phenomena that cannot be easily reproduced in a laboratory environment because, for example, they are dangerous (e.g., fire, radiations). We realised an augmented reality system for robots in which a simulator retrieves real-time data on the real environment through a multi-camera tracking system and delivers post-processed information to the robot swarm according to each robot's sensing range. We illustrate the proposed virtual sensing technology through an experiment involving 15 e-pucks.

Abstract. We present an experiment in automatic design of robot swarms. For the first time in the swarm robotics literature, we perform an objective comparison of multiple design methods: we compare swarms designed by two automatic methods-AutoMoDe-Vanilla and EvoStickwith swarms manually designed by human experts. AutoMoDe-Vanilla and EvoStick have been previously published and tested on two tasks. To evaluate their generality, in this paper we test them without any modification on five new tasks. Besides confirming that AutoMoDe-Vanilla is e↵ective, our results provide new insight into the design of robot swarms. In particular, our results indicate that, at least under the adopted experimental protocol, not only does automatic design su↵er from the reality gap, but also manual design. The results also show that both manual and automatic methods benefit from bias injection. In this work, bias injection consists in restricting the design search space to the combinations of pre-existing modules. The results indicate that bias injection helps to overcome the reality gap, yielding better performing robot swarms.