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

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Abstract-We propose ASEBA, a modular architecture for event-based control of complex robots. ASEBA runs scripts inside virtual machines on self-contained sensor and actuator nodes. This distributes processing with no loss of versatility and provides several benefits. The closeness to the hardware allows fast reactivity to environmental stimuli. The exploitation of peripheral processing power to filter raw data offloads any central computer and thus allows the integration of a large number of peripherals. Thanks to scriptable and plug-and-play modules, ASEBA provides instant compilation and real-time monitoring and debugging of the behavior of the robots. Our results show that ASEBA improves the performance of the behavior with respect to other architectures. For instance, doing obstacle avoidance on the marXbot robot consumes two orders of magnitude less bandwidth than using a polling-based architecture. Moreover, latency is reduced by a factor of two to three. Our results also show how ASEBA enables advanced behavior in demanding environments using a complex robot, such as the handbot robot climbing a shelf to retrieve a book.

Abstract-We present a cooperative navigation algorithm for robotic swarms. Its purpose is to let a robot find a given target robot, while being guided by the other robots of the swarm. The system is based on wireless communication: the robots forward messages containing navigation information over the ad hoc network among them, and the searching robot uses this information to find its target. We study the algorithm in two different scenarios. In the first scenario, a single searching robot needs to find a single target, while all other robots are involved in tasks of their own. We show that the communication based navigation system allows the robots of the swarm to guide the searching robot without the need to adapt their own movements. In the second scenario, we study collective navigation: all robots of the swarm need to navigate back and forth between two targets. We show that in this case, the proposed navigation algorithm gives rise to synergies in robot navigation, and lets the swarm self-organize into a robust dynamic structure. This selforganization improves navigation efficiency, and lets the swarm find shortest paths in cluttered environments. We test our system both in simulation and on real robots.

This article considers the suitability of current robots designed to assist humans in accomplishing their daily domestic tasks. With several million units sold worldwide, robotic vacuum cleaners are currently the figurehead in this field. As such, we will use them to investigate the following key question: Could a robot possibly replace the hand-operated vacuum cleaner? One must consider not just how well a robot accomplishes its task, but also how well it integrates inside the user's space and perception. We took a holistic approach to addressing these topics by combining two studies in order to build a common ground. In the first of these studies, we analyzed a sample of seven robots to identify the influence of key technologies, like the navigation system, on performance. In the second study, we conducted an ethnographic study within nine households to identify users' needs. This innovative approach enables us to recommend a number of concrete improvements aimed at fulfilling users' needs by leveraging current technologies to reach new possibilities.