Conference Paper

The SWARM-BOTS Project.

DOI: 10.1007/978-3-540-30552-1_4 Conference: Swarm Robotics, SAB 2004 International Workshop, Santa Monica, CA, USA, July 17, 2004, Revised Selected Papers
Source: DBLP

ABSTRACT This paper provides an overview of the SWARM-BOTS project, a robotic project sponsored by the Future and Emerging Technologies
program of the European Commission. The paper illustrates the goals of the project, the robot prototype and the 3D simulator
we built. It also reports on the results of experimental work in which distributed adaptive controllers are used to control
a group of real, or simulated, robots so that they perform a variety of tasks which require cooperation and coordination.

1 Bookmark
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Designing an adaptive multi-agent system often requires the specification of interaction patterns between the different agents. To date, it remains unclear to what extent such interaction patterns influence the dynamics of the learning mechanisms inherent to each agent in the system. Here, we address this fundamental problem, both analytically and via computer simulations, examining networks of agents that engage in stag-hunt games with their neighbors and thereby learn to coordinate their actions. We show that the specific network topology does not affect the game strategy the agents learn on average. Yet, network features such as heterogeneity and clustering effectively determine how this average game behavior arises and how it manifests itself. Network heterogeneity induces variation in learning speed, whereas network clustering results in the emergence of clusters of agents with similar strategies. Such clusters also form when the network structure is not predefined, but shaped by the agents themselves. In that case, the strategy of an agent may become correlated with that of its neighbors on the one hand, and with its degree on the other hand. Here, we show that the presence of such correlations drastically changes the overall learning behavior of the agents. As such, our work provides a clear-cut picture of the learning dynamics associated with networks of agents trying to optimally coordinate their actions.
    Adaptive Behavior 10/2010; 18(5):416-427. · 1.11 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Many applications of swarm robotics require autonomous navigation in unknown environments. We describe a new collective navigation strategy based on diffusion limited aggregation and bacterial foraging behaviour. Both methods are suitable for typical swarm robots as they require only minimal sensory and control capabilities. We demonstrate the usefulness of the strategy with a swarm that is capable of autonomously finding charging stations and show that the collective search can be significantly more effective than individual-based search.
    Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II; 09/2012
  • [Show abstract] [Hide abstract]
    ABSTRACT: Swarm robotics system has been a particularly active topic of robotics in recent years due to the increasing deepening of research on robotics technology and application. This paper gives a survey of swarm robotics system research from such aspects as theoretical basis and physical research, simulation platform, distributed control information fusion and communications system. Some problems that need to be solved about swarm robotics system research in IOT (Internet of Things) environment are also raised, such as co-adaptation, distributed control and self-organization, resource scheduling management. Finally, the ant colony algorithm and particle swarm optimization are applied to the swarm robotics system.
    Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I; 06/2012

Full-text (2 Sources)

Available from
May 22, 2014