Many modern sensors used for mapping produce 3D point clouds, which are typically registered together using the iterative closest point (icp) algorithm. Because icp has many variants whose performances depend on the environment and the sensor, hundreds of variations have been published. However, no comparison frameworks are available leading to arduous selection of an appropriate variant for particular experimental conditions. The first contribution of this paper consists of a protocol that allows for a comparison between icp variants, taking into account a broad range of inputs. The second contribution is an open-source icp library, which is fast enough to be usable in multiple real-world applications, while being modular enough to ease comparison This work was supported by the EU FP7 IP projects Natural Human-Robot Cooperation in Dynamic Environments (ICT-247870) and myCopter (FP7-AAT-2010-RTD-1). F. Pomerleau was supported by a fellowship from the Fonds québécois de recherche sur la nature et les technologies (FQRNT).

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

Information transfer plays a central role in the biology of most organisms, particularly social species [1, 2]. Although the neurophysiological processes by which signals are produced, conducted, perceived, and interpreted are well understood, the conditions conducive to the evolution of communication and the paths by which reliable systems of communication become established remain largely unknown. This is a particularly challenging problem because efficient communication requires tight coevolution between the signal emitted and the response elicited [3]. We conducted repeated trials of experimental evolution with robots that could produce visual signals to provide information on food location. We found that communication readily evolves when colonies consist of genetically similar individuals and when selection acts at the colony level. We identified several distinct communication systems that differed in their efficiency. Once a given system of communication was well established, it constrained the evolution of more efficient communication systems. Under individual selection, the ability to produce visual signals resulted in the evolution of deceptive communication strategies in colonies of unrelated robots and a concomitant decrease in colony performance. This study generates predictions about the evolutionary conditions conducive to the emergence of communication and provides guidelines for designing artificial evolutionary systems displaying spontaneous communication.

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The efficiency of social insect colonies critically depends on their ability to efficiently allocate workers to the various tasks which need to be performed. While numerous models have investigated the mechanisms allowing an efficient colony response to external changes in the environment and internal perturbations, little attention has been devoted to the genetic architecture underlying task specialization. We used artificial evolution to compare the performances of three simple genetic architectures underlying withincolony variation in response thresholds of workers to five tasks. In the 'deterministic mapping' system, the thresholds of individuals for each of the five tasks is strictly genetically determined. In the second genetic architecture ('probabilistic mapping'), the genes only influence the probability of engaging in one of the tasks. Finally, in the 'dynamic mapping' system, the propensity of workers to engage in one of the five tasks depends not only on their own genotype, but also on the behavioural phenotypes of other colony members. We found that the deterministic mapping system performed well only when colonies consisted of unrelated individuals and were not subjected to perturbations in task allocation. The probabilistic mapping system performed well for colonies of related and unrelated individuals when there were no perturbations. Finally, the dynamic mapping system performed well under all conditions and was much more efficient than the two other mapping systems when there were perturbations. Overall, our simulations reveal that the type of mapping between genotype and individual behaviour greatly influences the dynamics of task specialization and colony productivity. Our simulations also reveal complex interactions between the mode of mapping, level of within-colony relatedness and risk of colony perturbations.

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.

Thymio II is a small robot developed for education. It aims at offering a wide public the possibility to understand the basics of robotics and programming. To achieve this, it aims at being appealing to a large age range and serve as a medium for several types of activities. In this study, we tested it in five different workshops of the EPFL Robotics Festival with various activities. The workshops target different age groups and the participants can control the robot via different means: builtin buttons, graphical programming and text programming. At the end of the activities, participants were asked to fill a short survey to give their impressions about the robot, their appreciation of the tasks and their motivations to take part. We could show through this feedback that Thymio II appeals to young children as much as to teenagers, to both girls and boys, and allows them to have fun and learn new things.

Abstract-The increasing number of ICP variants leads to an explosion of algorithms and parameters. This renders difficult the selection of the appropriate combination for a given application. In this paper, we propose a state-of-the-art, modular, and efficient implementation of an ICP library. We took advantage of the recent availability of fast depth cameras to demonstrate one application example: a 3D pose tracker running at 30 Hz. For this application, we show the modularity of our ICP library by optimizing the use of lean and simple descriptors in order to ease the matching of 3D point clouds. This tracker is then evaluated using datasets recorded along a ground truth of millimeter accuracy. We provide both source code and datasets to the community in order to accelerate further comparisons in this field.