The swarm intelligence paradigm has proven to have very interesting properties such as robustness, flexibility and ability to solve complex problems exploiting parallelism and self-organization. Several robotics implementations of this paradigm confirm that these properties can be exploited for the control of a population of physically independent mobile robots. The work

Summary. Multi-robot systems have been studied in tasks that require the robots to be physically linked. In such a configuration, a group of robots may navigate a terrain that proves too difficult for a single robot. On the contrary, many collective tasks can be accomplished more efficiently by a group of independent robots. This paper is about swarm-bot, a robotic system that can operate in both configurations and autonomously switch from one to the other. We examine the performance of a single robot and of groups of robots selfassembling with an object or another robot. We assess the robustness of the system with respect to different types of rough terrain. Finally, we evaluate the performance of swarms of 16 physical robots. At present, for self-assembly in autonomous, mobile robotics, swarm-hots is the state of the art for what concerns reliability, robustness and speed.

Abstract. This paper addresses the cooperative transport of a heavy object, called prey, towards a sporadically changing target location by a group of robots. The study is focused on the situation in which some robots are given the opportunity to localize the target, while the others (called the blind ones) are not. We propose the use of relatively simple robots capable of self-assembling into structures which pull or push the prey. To enable a blind robot to contribute to the group’s performance, it can locally perceive traction forces, and whether it is moving or not. The robot group is controlled in a distributed manner, using a modular control architecture. A collection of simple hand-coded and artificially evolved control modules is presented and discussed. For group sizes ranging from 2 to 16 and different proportions of blind robots within the group, it is shown that controlled by an evolved solution, blind robots make an essential contribution to the group’s performance. The study is carried out using a physics-based simulation of a real robotic system that is currently under construction. 1

This article illustrates the methods and results of two sets of experiments in which a group of mobile robots, called s-bots, are required to physically connect to each other, that is, to self-assemble, to cope with environmental conditions that prevent them from carrying out their task individually. The first set of experiments is a pioneering study on the utility of self-assembling robots to address relatively complex scenarios, such as cooperative object transport. The results of our work suggest that the s-bots possess hardware characteristics which facilitate the design of control mechanisms for autonomous self-assembly. The control architecture we developed proved particularly successful in guiding the robots engaged in the cooperative transport task. However, the results also showed that some features of the robots ’ controllers had a disruptive effect on their performances. The second set of experiments is an attempt to enhance the adaptiveness of our multi-robot system. In particular, we aim to synthesise an integrated (i.e., not-modular) decisionmaking mechanism which allows the s-bot to autonomously decide whether or not environmental contingencies require self-assembly. The results show that it is possible to synthesize, by using evolutionary computation techniques, artificial neural networks that integrate both the mechanisms for sensory-motor coordination and for decision making required by the robots in the context of self-assembly. This work was supported by the SWARM-BOTS project, funded by the Future and Emerging Technologies

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Self-organization is a great concept for building scalable systems consisting of huge numbers of subsystems. The primary objectives are coordination and collaboration on a global goal. Until now, many self-organization methods have been developed for communication networks in general and ad hoc networks in particular. Nevertheless, the term self-organization is still often misunderstood or misused. This paper contributes to the ad hoc community by providing a better understanding of self-organization in ad hoc networks. Primarily, solutions for the medium access contol and the network layer are analyzed and discussed. The main contribution of this paper is a categorization of self-organization methodologies. Additionally, well-known methods in ad hoc networks are classified and some case studies are provided.

Abstract. This paper addresses the design of control policies for groups of up to 16 simple autonomous mobile robots (called s-bots) for the cooperative transport of heavy objects of different shapes and sizes. The s-bots are capable of establishing physical connections with each other and with the object (called prey). We want the s-bots to self-assemble into structures which pull or push the prey towards a target location. The s-bots are controlled by neural networks that are shaped by artificial evolution. The evolved controllers perform quite well, independently of the shape and size of the prey, and allow the group to transport the prey towards a moving target. Additionally, the controllers evolved for a relatively small group can be applied to larger groups, making possible the transport of heavier prey. Experiments are carried out using a physics simulator, which provides a realistic simulation of real robots, which are currently under construction. 1

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In this article, we analyze the behavior of a group of robots involved in an object retrieval task. The robots ’ control system is inspired by a model of ants ’ foraging. This model emphasizes the role of learning in the individual. Individuals adapt to the environment using only locally available information. We show that a simple parameter adaptation is an effective way to improve the efficiency of the group and that it brings forth division of labor between the members of the group. Moreover, robots that are best at retrieving have a higher probability of becoming active retrievers. This selection of the best members does not use any explicit representation of individual capabilities. We analyze this system and point out its strengths and its weaknesses.

In this paper, we aim to design decision-making mechanisms for an autonomous robot equipped with simple sensors, which integrates over time its perceptual experience in order to initiate a simple signalling response. Contrary to other previous similar studies, in this work the decision-making is uniquely controlled by the time-dependent structures of the agent controller, which in turn, are tightly linked to the mechanisms for sensory-motor coordination. The results of this work show that a single dynamic neural network, shaped by evolution, makes an autonomous agent capable of “feeling” time through the flow of sensations determined by its actions. Further analysis of the evolved solutions reveals the nature of the selective pressures which facilitate the evolution of fully discriminating and signalling agents. Moreover, we show that, by simply working on the nature of the fitness function, it is possible to bring forth discrimination mechanisms which are robust enough to deal with environmental circumstances that have never been encountered by the best evolved robot’ ancestors.