Swarm robotics draws inspiration from decentralized self-organizing biological systems in general and from the collective behavior of social insects in particular. In social insect colonies, many tasks are performed by higher order group or team entities, whose task-solving capacities transcend those of the individual participants. In this paper, we investigate the emergence of such higher order entities. We report on an experimental study in which a team of physical robots performs a foraging task. The robots are "identical" in hardware and control. They make little use of memory and take actions purely on the basis of local information. Our study advances the current state of the art in swarm robotics with respect to the number of real-world robots engaging in teamwork (up to 12 robots in the most challenging experiment). To the best of our knowledge, in this paper we present the first self-organized system of robots that displays a dynamical hierarchy of teamwork (with cooperation also occurring among higher order entities). Our study shows that teamwork requires neither individual recognition nor differences between individuals. This result might also contribute to the ongoing debate on the role of these characteristics in the division of labor in social insects.

We present two swarm intelligence control mechanisms used for distributed robot path formation. In the first, the robots form linear chains. We study three variants of robot chains, which vary in the degree of motion allowed to the chain structure. The second mechanism is called vectorfield. In this case, the robots form a pattern that globally indicates the direction towards a goal or home location.We test each controller on a task that consists in forming a path between two objects which an individual robot cannot perceive simultaneously. Our simulation experiments show promising results. All the controllers are able to form paths in complex obstacle environments and exhibit very good scalability, robustness, and fault tolerance characteristics. Additionally, we observe that chains perform better for small robot group sizes, while vectorfield performs better for large groups.

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Foraging robots involved in a search and retrieval task may create paths to navigate faster in their environment. In this context, a swarm of robots that has found several resources and created different paths may benefit strongly from path selection. Path selection enhances the foraging behavior by allowing the swarm to focus on the most profitable resource with the possibility for unused robots to stop participating in the path maintenance and to switch to another task. In order to achieve path selection, we implement virtual ants that lay artificial pheromone inside a network of robots. Virtual ants are local messages transmitted by robots; they travel along chains of robots and deposit artificial pheromone on the robots that are literally forming the chain and indicat- ing the path. The concentration of artificial pheromone on the robots allows them to decide whether they are part of a selected path. We parameterize the mechanism with a mathematical model and provide an experimental validation using a swarm of 20 real robots. We show that our mechanism favors the selection of the closest resource is able to select a new path if a selected resource becomes unavailable and selects a newly detected and better resource when possible. As robots use very simple messages and behaviors, the system would be particularly well suited for swarms of microrobots with minimal abilities.

Abstract. In this paper we analyse a previously introduced swarm intelligence control mechanism used for solving problems of robot path formation. We determine the impact of two probabilistic control parameters. In particular, the problem we consider consists in forming a path between two objects which an individual robot cannot perceive simultaneously. Our experiments were conducted in simulation. We compare four different robot group sizes with up to 20 robots, and vary the difficulty of the task by considering five different distances between the objects which have to be connected by a path. Our results show that the two investigated parameters have a strong impact on the behaviour of the overall system and that the optimal set of parameters is a function of group size and task difficulty. Additionally, we show that our system scales well with the number of robots.

In social insect colonies, many tasks are performed by higherorder entities, such as groups and teams whose task solving capacities transcend those of the individual participants. In this paper, we investigate the emergence of such higher-order entities using a colony of up to 12 physical robots. We report on an experimental study in which the robots engage in a range of different activities, including exploration, path formation, recruitment, self-assembly and group transport. Once the robots start interacting with each other and with their environment, they selforganise into teams in which distinct roles are performed concurrently. The system displays a dynamical hierarchy of teamwork, the cooperating elements of which comprise higher-order entities. The study shows that teamwork requires neither individual recognition nor inter-individual differences, and as such might contribute to the ongoing debate on the role of such characteristics for the division of labour in social insects.

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Abstract. In this paper, we study the cooperative transport of a heavy object by a group of robots towards a goal. We investigate the case in which robots have partial and noisy knowledge of the goal direction and can not perceive the goal itself. The robots have to coordinate their motion to apply enough force on the object to move it. Furthermore, the robots should share knowledge in order to collectively improve their estimate of the goal direction and transport the object as fast and as accurately as possible towards the goal.We propose a bio-inspired mechanism of negotiation of direction that is fully distributed. Four different strategies are implemented and their performances are compared on a group of four real robots, varying the goal direction and the level of noise. We identify a strategy that enables efficient coordination of motion of the robots. Moreover, this strategy lets the robots improve their knowledge of the goal direction. Despite significant noise in the robots' communication, we achieve effective cooperative transport towards the goal and observe that the negotiation of direction entails interesting properties of robustness.