this paper we present a set of experiments in which a group of simulated robots were evolved for the ability to aggregate and to move together toward a light target

In this paper we address the problem of how a group of four assembled simulated robots forming a linear structure can co-ordinate and move as straight and as fast as possible. This problem is solved in a rather simple and effective way by providing the robots with a sensor that detects the direction and intensity of the traction that the turret exerts on the chassis of each robot and by evolving their neural controllers. We also show how the evolved robots are able to generalize their ability in rather different circumstance by: (a) producing coordinated movements in teams with varying size, topology, and type of links; (b) displaying individual or collective obstacle avoidance behaviors when placed in an environment with obstacles; (c) displaying object pushing/pulling behavior when connected to or around a given object.

In this paper, we study coordinated motion in a swarm robotic system, called a swarm-bot. A swarm-bot is a self-assembling and self-organising artifact, composed of a swarm of s-bots, mobile robots with the ability to connect to and disconnect from each other. The swarm-bot concept is particularly suited for tasks that require all-terrain navigation abilities, such as space exploration or rescue in collapsed buildings. As a first step toward the development of more complex control strategies, we investigate the case in which a swarm-bot has to explore an arena while avoiding falling into holes. In such a scenario, individual s-bots have sensory-motor limitations that prevent them navigating efficiently. These limitations can be overcome if the s-bots are made to cooperate. In particular, we exploit the s-bots’ ability to physically connect to each another. In order to synthesise the s-bots’ controller, we rely on artificial evolution, which we show to be a powerful tool for the production of simple and effective solutions to the hole avoidance task.

Distributed coordination of groups of individuals accomplishing a common task without leaders, with little communication, and on the basis of selforganising principles, is an important research issue within the study of collective behaviour of animals, humans and robots. The paper shows how distributed coordination allows a group of evolved physicallylinked simulated robots (inspired by a robot under construction) to display a variety of highly coordinated basic behaviours such as collective motion, collective obstacle avoidance, and collective light approaching, and to integrate them in a coherent fashion. In this way the group is capable of searching and approaching a light target in an environment scattered with obstacles, furrows, and holes, where robots acting individually fail. The paper shows how the emerged coordination of the group relies upon robust self-organising principles (e.g. positive feedback) based on a special sensor that allows the single robots to perceive the “average ” group’s motion direction. The paper also presents a robust solution to a difficult coordination problem, that might also be encountered by some organisms, caused by the fact that the robots have to be capable of moving in any direction while being physically connected. Finally, the paper shows how the evolved distributed coordination mechanisms scale very well with respect to the number of robots, the way in which robots are assembled, the structure of the environment, and several other aspects. 1.

Abstract In this paper, we study coordinated motion in a swarm robotic system,

In certain tasks such as pursuit and evasion, multiple agents need to coordinate their behavior to achieve a common goal. An interesting question is, how can such behavior be best evolved? A powerful approach is to control the agents with neural networks, coevolve them in separate subpopulations, and test them together in the common task. In this paper, such a method, called Multi-Agent ESP (Enforced SubPopulations), is proposed and demonstrated in a prey-capture task. First, the approach is shown more efficient than evolving a single central controller for all agents. Second, cooperation is found to be most efficient through stigmergy, i.e. through role-based responses to the environment, rather than direct communication between the agents. Together these results suggest that role-based cooperation is an effective strategy in certain multi-agent domains. [ This paper is a revision of AI01-287.]

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Abstract — In cooperative multiagent systems, agents interact to solve tasks. Global dynamics of multiagent teams result from local agent interactions, and are complex and difficult to predict. Evolutionary computation has proven a promising approach to the design of such teams. The majority of current studies use teams composed of agents with identical control rules (“genetically homogeneous teams”) and select behavior at the team level (“team-level selection”). Here we extend current approaches to include four combinations of genetic team composition and level of selection. We compare the performance of genetically homogeneous teams evolved with individual-level selection, genetically homogeneous teams evolved with team-level selection, genetically heterogeneous teams evolved with individual-level selection, and genetically heterogeneous teams evolved with team-level selection. We use a simulated foraging task to show that the optimal combination depends on the amount of cooperation required by the task. Accordingly, we distinguish between three types of cooperative tasks and suggest guidelines for the optimal choice of genetic team composition and level of selection. Index Terms — Altruism, artificial evolution, cooperation, evolutionary robotics, fitness allocation, multiagent systems (MAS),

In this work, we introduce a swarm robotic system, called a swarm-bot. A swarm-bot is a self-assembling and self-organizing artifact composed of a swarm of s-bots, mobile robots with the ability to connect to/disconnect from each other. In particular, we address the problem of synthesizing controllers for the swarm-bot using Artificial Evolution. We describe the motivation behind the choice of the evolutionary approach and we provide examples of its application, detailing the results obtained in di#erent tasks, namely coordinated motion and hole avoidance. We show how evolution is able to produce simple but e#ective solutions, which lead to the emergence of self-organization in the swarm-bot.