A Novel Concept for the Study of Heterogeneous Robotic Swarms warm robotics systems are characterized by decentralized control, limited communication between robots, use of local information, and emergence of global behavior. Such systems have shown their potential for flexibility and robustness [1]-[3]. However, existing swarm robotics systems are by and large still limited to displaying simple proof-of-concept behaviors under laboratory conditions. It is our contention that one of the factors holding back swarm robotics research is the almost universal insistence on homogeneous system components. We believe that swarm robotics designers must embrace heterogeneity if they ever want swarm robotics systems to approach the complexity required of real-world systems. To date, swarm robotics systems have almost exclusively comprised physically and behaviorally undifferentiated agents. This design decision has its roots in ethological models of self-organizing natural systems. These models serve as inspiration for swarm robotics system designers, but are often highly abstract simplifications of natural systems and, to date, have largely assumed homogeneous agents. Selected dynamics of the systems under study are shown to emerge from the interactions of identical system components, ignoring the heterogeneities (physical, spatial, functional, and informational) that one can find in almost any natural system. The field of swarm robotics currently lacks methods and tools with which to study and leverage the heterogeneity that is present in natural systems. To remedy this deficiency, we propose swarmanoid, an innovative swarm robotics system composed of three different robot types with complementary skills: foot-bots are small autonomous robots specialized in moving on both even and uneven terrains, capable of self-assembling and of transporting objects or other robots; hand-bots are autonomous robots capable of climbing some vertical surfaces and manipulating small objects; and eye-bots are autonomous flying robots that can attach to an indoor ceiling, capable of analyzing the environment from a privileged position to S

In this paper, we study the problem of exploration and navigation in an unknown environment from an evolutionary swarm robotics perspective. In other words, we search for an efficient exploration and navigation strategy for a swarm of robots, which exploits cooperation and self-organisation to cope with the limited abilities of the individual robots. The task faced by the robots consists in the exploration of an unknown environment in order to find a path between two distant target areas. The collective strategy is synthesised through evolutionary robotics techniques, and is based on the emergence of a dynamic structure formed by the robots moving back and forth between the two target areas. Due to this structure, each robot is able to maintain the right heading and to efficiently navigate between the two areas. The evolved behaviour proved to be effective in finding the shortest path, adaptable to new environmental conditions, scalable to larger groups and larger environment size, and robust to individual failures.

The effects of striatal dopamine (DA) on behavior have been widely investigated over the past decades, with “phasic” burst firings considered as the key expression of a reward prediction error responsible for reinforcement learning. Less well studied is “tonic” DA, where putative functions include the idea that it is a regulator of vigor, incentive salience, disposition to exert an effort and a modulator of approach strategies. We present a model combining tonic and phasic DA to show how different outflows triggered by either intrinsically or extrinsically motivating stimuli dynamically affect the basal ganglia by impacting on a selection process this system performs on its cortical input. The model, which has been tested on the simulated humanoid robot iCub interacting with a mechatronic board, shows the putative functions ascribed to DA emerging from the combination of a standard computational mechanism coupled to a differential sensitivity to the presence of DA across the striatum.

A well known problem in the design of the control system for a swarm of robots concerns the definition of suitable individual rules that result in the desired coordinated behaviour. A possible solution to this problem is given by the automatic synthesis of the individual controllers through evolutionary or learning processes. These processes offer the possibility to freely search the space of the possible solutions for a given task, under the guidance of a user-defined utility function. Nonetheless, there exist no general principles to follow in the definition of such a utility function in order to reward coordinated group behaviours. As a consequence, task dependent functions must be devised each time a new coordination problem is under study. In this paper, we propose the use of measures developed in Information Theory as task-independent, implicit utility functions. We present two experiments in which three robots are trained to produce generic coordinated behaviours. Each robot is provided with rich sensory and motor apparatus, which can be exploited to explore the environment and to communicate with other robots. We show how coordinated behaviours can be synthesised through a simple evolutionary process. The only criteria used to evaluate the performance of the robotic group is the estimate of mutual information between the motor states of the robots. Electronic supplementary materialThe online version of this article (http://dx.