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 flocking, a swarm of robots moves cohesively in a common direction. Traditionally, flocking is realized using two main control rules: proximal control, which controls the cohesion of the swarm using local range-and-bearing information about neighboring robots; and alignment control, which allows the robots to align in a common direction and uses more elaborate sensing mechanisms to obtain the orientation of neighboring robots. So far, limited attention has been given to motion control, used to translate the output of these two control rules into robot motion.In this paper, we propose a novel motion control method: magnitude-dependent motion control (MDMC). Through simulations and real robot experiments, we show that, with MDMC, flocking in a random direction is possible without the need of alignment control and of robots having a preferred direction of travel. MDMC has the advantage to be implementable on very simple robots that lack the capability to detect the orientation of their neighbors. Additionally, we introduce a small proportion of robots informed about a desired direction of travel. We compare MDMC with a motion control method used in previous robotics literature, which we call magnitudeindependent motion control (MIMC), and we show that the swarms can travel longer distances in the desired direction when using MDMC instead of MIMC. Finally, we systematically study flocking under various conditions: with or without alignment control, with or without informed robots, with MDMC or with MIMC.

We propose a self-adaptive communication strategy for controlling the heading direction of a swarm of mobile robots during flocking. We consider the problem where a small group of informed robots has to guide a large swarm along a desired direction. We consider three versions of this problem: one where the desired direction is fixed; one where the desired direction changes over time; one where a second group of informed robots has information about a second desired direction that conflicts with the first one, but has higher priority. The goal of the swarm is to follow, at all times, the desired direction that has the highest priority and, at the same time, to keep cohesion.The proposed strategy allows the informed robots to guide the swarm when only one desired direction is present. Additionally, a self-adaptation mechanism alThis work was partially supported by the European Union through the ERC Advanced Grant "E-SWARM: Engineering Swarm Intelligence Systems" (contract 246939) and the Future and Emerging Technologies project ASCENS and by the Vlaanderen Research Foundation Flanders (Flemish Community of Belgium) through the H2Swarm project. The information provided is the sole responsibility of the authors and does not reflect the European Commission's opinion. The European Commission is not responsible for any use that might be made of data appearing in this publication. Mauro Birattari, and Marco Dorigo acknowledge support from the F.R.S. lows the robots to indirectly sense the second desired direction, and makes the swarm follow it. In experiments with both simulated and real robots, we evaluate how well the swarm tracks the desired direction and how well it maintains cohesion. We show that, using self-adaptive communication, the swarm is able to follow the desired direction with the highest priority at all times without splitting.

Self-assembling robots have the potential to undergo autonomous morphological adaptation. However, due to the simplicity in their hardware makeup and their limited perspective of the environment, self-assembling robots are often not able to reach their potential and adapt their morphologies to tasks or environments without external cues or prior information. In this paper, we present supervised morphogenesis-a control methodology that makes self-assembling robots truly flexible by enabling aerial robots to exploit their elevated position and better view of the environment to initiate and control (hence supervise) morphology formation on the ground. We present results of two case studies in which we assess the feasibility of the presented methodology using real robotic hardware. In the case studies, we implemented supervised morphogenesis using two different aerial platforms and up to six self-assembling autonomous robots. We furthermore quantify the benefits attainable for self-assembling robots through cooperation with aerial robots using simulation-based studies. The research presented in this paper is a significant step towards realizing the true potential of self-assembling robots by enabling autonomous morphological adaptation to a priori unknown tasks and environments.