Stéphane Magnenat

Evolutionary Conditions for the Emergence of Communication in Robots

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.

The marXbot, a miniature mobile robot opening new perspectives for the collective-robotic research

Collective and swarm robotics explores scenarios involving many robots running at the same time. A good platform for collective-robotic experiments should provide certain features among others: it should have a large battery life, it should be able to perceive its peers, and it should be capable of interacting with them. This paper presents the marXbot, a miniature mobile robot that addresses these needs. The marXbot uses differential-drive treels to provide rough-terrain mobility. The marXbot allows continuous experiments thanks to a sophisticated energy management and a hotswap battery exchange mechanism. The marXbot can self-assemble with peers using a compliant attachment mechanism. The marXbot provides high-quality vision, using two cameras directly interfaced with an ARM processor. Compared to the related work, the marXbot has better energy management, vision, and interaction capabilities. By allowing complex tasks in large environments for long durations, the marXbot opens new perspectives for the collective-robotic research.

ASEBA: A Modular Architecture for Event-Based Control of Complex Robots

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. Due 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.

Division of labour and colony efficiency in social insects: effects of interactions between genetic architecture, colony kin structure and rate of perturbations

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 within-colony 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.

The Hand-Bot, a Robot Design for Simultaneous Climbing and Manipulation

We present a novel approach to mobile object manipulation for service in indoor environments. Current research in service robotics focus on single robots able to move, manipulate objects, and transport them to various locations. Our approach differs by taking a collective robotics perspective: different types of small robots perform different tasks and exploit complementarity by collaborating together. We propose a robot design to solve one of these tasks: climbing vertical structures and manipulating objects. Our robot embeds two manipulators that can grasp both objects or structures. To help climbing, it uses a rope to compensate for the gravity force. This allows it to free one of its manipulators to interact with an object while the other grasps a part of a structure for stabilization. Our robot can launch and retrieve the rope autonomously, allowing multiple ascents. We show the design and the implementation of our robot and demonstrate the successful autonomous retrieval of a book from a shelf.

Superlinear physical performances in a SWARM-BOT

A swarm-bot is a robotic entity built of several autonomous mobile robots (called s-bots) physically connected together. This form of collective robotics exploits robot interactions both at the behavioral and physical levels. The goal of this paper is to analyze the physical performance of a swarm-bot as function of its size (number n of s-bots composing it). We present three tasks and the corresponding swarm-bot performances. In all three tasks we show superlinear performances in a range of n where the physical forces applied in the structure fit to the robot design. This superlinear performance range helps in understanding which swarm-bot size is optimal for a given task and gives interesting hints for the design of new application-oriented swarm-bots.

Cooperative navigation in robotic swarms

We study cooperative navigation for robotic swarms in the context of a general event-servicing scenario. In the scenario, one or more events need to be serviced at specific locations by robots with the required skills. We focus on the question of how the swarm can inform its members about events, and guide robots to event locations. We propose a solution based on delay-tolerant wireless communications: by forwarding navigation information between them, robots cooperatively guide each other towards event locations. Such a collaborative approach leverages on the swarm’s intrinsic redundancy, distribution, and mobility. At the same time, the forwarding of navigation messages is the only form of cooperation that is required. This means that the robots are free in terms of their movement and location, and they can be involved in other tasks, unrelated to the navigation of the searching robot. This gives the system a high level of flexibility in terms of application scenarios, and a high degree of robustness with respect to robot failures or unexpected events. We study the algorithm in two different scenarios, both in simulation and on real robots. 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. In the second scenario, we study collective navigation: all robots of the swarm navigate back and forth between two targets, which is a typical scenario in swarm robotics. We show that in this case, the proposed algorithm gives rise to synergies in robot navigation, and it lets the swarm self-organize into a robust dynamic structure. The emergence of this structure improves navigation efficiency and lets the swarm find shortest paths.

Affordable SLAM through the co-design of hardware and methodology

Simultaneous localization and mapping (SLAM) is a prominent feature for autonomous robots operating in undefined environments. Applications areas such as consumer robotics appliances would clearly benefit from low-cost and compact SLAM implementations. The SLAM research community has developed several robust algorithms in the course of the last two decades. However, until now most SLAM demonstrators have relied on expensive sensors or large processing power, limiting their realms of application. Several works have explored optimizations into various directions; however none has presented a global optimization from the mechatronic to the algorithmic level.

Design of Magnetic Switchable Device (MSD) and applications in climbing robot

A Magnetic Switchable Device (MSD) is a ferromagnetic circuit using permanent magnets where the flux can circulate between different paths when its configuration is changed. This routes or cancels the flux through specific surfaces, and thus turns on or off adhesion forces.We present classic and innovative magnetic configuration to realize powerful MSD. We designed and prototyped some miniature systems and give their characteristics. Finally various robotics applications for gripper, anchor and climbing robot are unveiled where the MSD solution has proved to be advantageous.

Bringing Robotics to Formal Education: The Thymio Open-Source Hardware Robot

Mobile robots are valuable tools for education because of both the enthusiasm they raise and the multidisciplinary nature of robotics technology. Mobile robots give access to a wide range of fields, such as complex mechanics, sensors, wireless transmission, mathematics, and computer science. However, despite their potential as educational tools, robots are still not as widespread in schools as they could be. In this article, we identify five key reasons: lack of diversity, high cost, noninclusive design, lack of educational material, and lack of stability over time. Then, we describe our answers to these problems, as we implemented them in the Thymio project: a mature mass-produced open-hardware robot, at a low price, with a multiage and gender-neutral feature set, and with a design promoting creativity, facilitating learning, and providing a wide range of interaction possibilities from built-in behaviors to text programming, passing through different visual programming environments. We highlight some neglected key issues that differentiate open-source hardware from open-source software, for instance the legal uncertainty of designing open hardware using professional computer-aided design (CAD) tools and the difficulty to distribute the development. Our solution to these being to increase the awareness of CAD editors to open-source hardware and to provide a two-layer development model for hardware.