We investigate the emergence of language convention within a swarm of robots foraging in an open environment from two identical resources. While foraging, the swarm needs to explore and decide which resource to exploit, moving through complex transitory dynamics towards different possible equilibria, such as, selection of a single resource or spread across the two. Our point of interest is the understanding of possible correlations between the emergent, evolving, task-induced interaction network and the language dynamics. In particular, our goal is to determine whether the dynamics of the interaction network are sufficient to determine emergent naming conventions that represent features of the task execution (e.g., choice of one or the other resource) and of the environment, In other words, we look for an emergent vocabulary that is both complete (a word for each resource) and correct (no misnomer) for as long as each resource is relevant to the swarm. In this study, robots are playing two variants of the minimal language game. The classic one, where words are created when needed, and a new variant we introduce in this article: the spatial minimal naming game, where the creation of words is linked with the discovery of resources by exploring robots. We end the article by proposing a proof of concept extension of the spatial minimal naming game that assures the completeness and correctness of the swarms vocabulary.
Modern cities are growing ecosystems that face new challenges due to the increasing population demands. One of the many problems they face nowadays is waste management, which has become a pressing issue requiring new solutions. Swarm robotics systems have been attracting an increasing amount of attention in the past years and they are expected to become one of the main driving factors for innovation in the field of robotics. The research presented in this paper explores the feasibility of a swarm robotics system in an urban environment. By using bio-inspired foraging methods such as multi-place foraging and stigmergy-based navigation, a swarm of robots is able to improve the efficiency and autonomy of the urban waste management system in a realistic scenario. To achieve this, a diverse set of simulation experiments was conducted using real-world GIS data and implementing different garbage collection scenarios driven by robot swarms. Results presented in this research show that the proposed system outperforms current approaches. Moreover, results not only show the efficiency of our solution, but also give insights about how to design and customize these systems.
We define the nervous system of a robot as the processing unit responsible for controlling the robot body, together with the links between the processing unit and the sensorimotor hardware of the robot - i.e., the equivalent of the central nervous system in biological organisms. We present autonomous robots that can merge their nervous systems when they physically connect to each other, creating a "virtual nervous system" (VNS). We show that robots with a VNS have capabilities beyond those found in any existing robotic system or biological organism: they can merge into larger bodies with a single brain (i.e., processing unit), split into separate bodies with independent brains, and temporarily acquire sensing and actuating capabilities of specialized peer robots. VNS-based robots can also self-heal by removing or replacing malfunctioning body parts, including the brain.