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We demonstrate the ability of a swarm of autonomous micro-robots to perform collective decision making in a dynamic environment. This decision making is an emergent property of decentralized self-organization, which results from executing a very simple bio-inspired algorithm. This algorithm allows the robotic swarm to choose from several distinct light sources in the environment and to aggregate in the area with the highest illuminance. Interestingly, these decisions are formed by the collective, although no information is exchanged by the robots. The only communicative act is the detection of robot-to-robot encounters. We studied the performance of the robotic swarm under four environmental conditions and investigated the dynamics of the aggregation behaviour as well as the flexibility and the robustness of the solutions. In summary, we can report that the tested robotic swarm showed two main characteristic features of swarm systems: it behaved flexible and the achieved solutions were very robust. This was achieved with limited individual sensor abilities and with low computational effort on each single robot in the swarm.

Abstract. Division of labour is one of the possible strategies to efficiently exploit the resources of autonomous systems. It is also a phenomenon often observed in animal systems. We show an architecture that implements division of labour in Sensor/Actuator Networks. The way the nodes take their decisions is inspired by ants ’ foraging behaviour. The preliminary results show that the architecture and the bio-inspired mechanism successfully induce self-organised division of labour in the network. The experiments were run in simulation. We developed a new type of simulator for this purpose. Key features of our work are crosslayer design and exploitation of inter-node interactions. No explicit negotiation between the agents takes place. 1

Abstract. This paper addresses the multi-agent patrolling problem, which consists for a set of autonomous agents to visit all the places of an unknown environment as regularly as possible. The proposed approach is based on the ant paradigm. Each agent can only mark and move according to its local perception of the environment. We study EVAW, a pheromone-based variant of the EVAP [3] and VAW [12]. The main novelty of the paper is the proof of some emergent spatial properties of the proposed algorithm. In particular we show that obtained cycles are necessarily of same length, which ensures an efficient spatial distribution of the agents. We also report some experimental results and discuss open questions concerning the proposed algorithm. 1

Interference reduction through task

Investigations of multi-robot systems often make implicit assumptions concerning the computational capabilities of the robots. Despite the lack of explicit attention to the computational capabilities of robots, two computational classes of robots emerge as the focal points of recent research: Robot Ants and robot Elephants. Ants have poor memory and communication capabilities, but are able to communicate using pheromones, in effect turning their work area into a shared memory. By comparison, Elephants are computationally stronger, have large memory, and are equipped with strong sensing and communication capabilities. Unfortunately, not much is known about the relation between the capabilities of these models in terms of the tasks they can address. In this paper, we present formal models of both Ants and Elephant, and investigate if one dominates the other. We present two algorithms: AntEater, which allows Elephant robots to execute ant algorithms; and ElephantGun, which converts elephant algorithms—specified as Turing machines—into ant algorithms. By exploring the computational capabilities of these algorithms, we reach interesting preliminary results regarding the computational power of both models. 1.

First and for most, I would like to thank my supervisor Prof. Alan FT Winfield for his guide and advise to complete this work. I really appreciate the freedom that Alan gave me in choosing the research direction and method. Along the way I have benefited a lot from the discussion with him, both from formal supervision meeting and informal research chatting every Friday lunch time. I would like especially to thank Alan for the help to correct all the grammars in English through the whole thesis with great patient. Without the help from Alan, this thesis couldn’t reach its final form. I am also grateful to my second supervisor Dr. Jin Sa for the insightful discussion about the thesis and the project. I want to thank Jin for personally supporting me in settling down in Bristol at the beginning of my study, which makes the life much easier. I would like to thank the director of the Bristol Robotics Laboratory, Prof. Chris Melhuish for providing an extremely friendly and stimulating research environment. I would like also to thank all the colleagues in the lab for all the suggestions and kindless help during last three years. A special thank goes to Jan Dyre Bjerknes for the useful and helpful discussion in swarm robotics, and for his organisation of all kinds of parties and activities.

Investigations of multi-robot systems often make implicit assumptions concerning the computational capabilities of the robots. Despite the lack of explicit attention to the computational capabilities of robots, two computational classes of robots emerge as focal points of recent research: Robot Ants and robot Elephants. Ants have poor memory and communication capabilities, but are able to communicate using pheromones, in effect turning their work area into a shared memory. By comparison, elephants are computationally stronger, have large memory, and are equipped with strong sensing and communication capabilities. Unfortunately, not much is known about the relation between the capabilities of these models in terms of the tasks they can address. In this paper, we present formal models of both ants and elephants, and investigate if one dominates the other. We present two algorithms: AntEater, which allows elephant robots to execute ant algorithms; and ElephantGun, which converts elephant algorithms—specified as Turing machines—into ant algorithms. By exploring the computational capabilities of these algorithms, we reach interesting conclusions regarding the computational power of both models.

In cooperative multi-agent systems, roles are used as a design concept when creating large systems, they are known to facilitate specialization of agents, and they can help to reduce interference in multi-robot domains. The types of tasks that the agents are asked to solve and the communicative capabilities of the agents significantly affect the way roles are used in cooperative multi-agent systems. Along with a discussion of these issues about roles in multi-agent systems, this article compares computational models of the role allocation problem, presents the notion of explicitly versus implicitly defined roles, gives a survey of the methods used to approach role allocation problems, and concludes with a list of open research questions related to roles in multi-agent systems.

Abstract—The goal of this work is to gain a better understanding of the role that inter-agent variation plays in selforganizing systems. We develop both continuous and discrete models of a multi-agent coordination procedure based on response thresholds and use these models to analyze the average behavior of a system as well as examine the dynamics of single instances of the problem. Results indicate that variation in the behaviors of agents can lead to increased stability and more effective self-organization in cooperative multi-agent systems. Keywords-self-organization; variation; multi-agent systems; emergent coordination I.