Generalizations of Evolved Decision-Making Mechanisms in Swarm Collective Perception: Communication range-Time-Accuracy trade-offs in swarm collective perception

This paper presents a study investigating the generalization characteristics of two neuro-controllers underpinning decision-making mechanisms in a swarm of robots engaged in a collective perception task. The neuro-controllers are both designed-using evolutionary computation-to operate in a randomly distributed cues environment, but under different conditions for what concerns the length of the com- munication range characterising the robots interactions. For one neuro- controller, the communication range during the design phase is 30cm, while for the other is 50 cm. The aim of the study is to explore the robust- ness and the limitations of the two distinct neuro-controllers across a range of different conditions and to establish the optimal bounds on the swarm communication range for this collective perception task. To exam- ine the performance of the two neuro-controllers we conduct a series of post-evaluations in 45 distinct environments, given by nine different dis- tributions of the perceptual cues, and five different communication ranges (i.e., 10, 20, 30, 40, and 50 cm). The results demonstrate that the neuro- controller evolved with a swarm communication range of 30cm gener- alizes better and exceeds the performance of the other neuro-controller evolved with 50 cm communication range in a vast majority of the post- evaluation conditions. However, the swarm performance degrades in con- ditions with patchily distributed perceptual cues and/or very short com- munication range.