� Self-Organization Introduction; system management and control; principles and characteristics; natural self-organization; methods and techniques � Networking Aspects: Ad Hoc and Sensor Networks Ad hoc and sensor networks; self-organization in sensor networks; evaluation criteria; medium access control; ad hoc routing; data-centric

This paper provides an overview of the SWARM-BOTS project, a robotics project sponsored by the Future and Emerging Technologies program of the European Commission (IST-2000-31010). We describe the s-bot, asmallautonomous robot with self-assembling capabilities that we designed and built within the project. Then we illustrate the cooperative object transport scenario that we chose to use as a test-bed for our robots. Last, we report on results of experiments in which a group of s-bots perform a variety of tasks within the scenario which may require selfassembling, physical cooperation and coordination. 1.

Robot foraging, a frequently used test application for collective robotics, consists in a group of robots retrieving a set of opportunely defined objects to a target location. A commonly observed experimental result is that the retrieving efficiency of the group of robots, measured for example as the number of units retrieved by a robot in a given time interval, tends to decrease with increasing group sizes. In this paper we describe a biology inspired method for tuning the number of foraging robots in order to improve the group efficiency. As a result of our experiments, in which robots use only locally available information and do not communicate with each other, we observe self-organised task allocation. This task allocation is effective in exploiting mechanical differences among the robots inducing specialisation in the robots activities.

This paper addresses qualitative and quantitative diversity and specialization issues in the framework of selforganizing, distributed, artificial systems. Both diversity and specialization are obtained via distributed learning from initially homogeneous swarms. While measuring diversity essentially quantifies differences among the individuals, assessing the degree of specialization implies correlation between the swarm's heterogeneity with its overall performance. Starting from the stick-pulling experiment in collective robotics, a task that requires the collaboration of two robots, we abstract and generalize in simulation the task constraints to k robots collaborating sequentially or in parallel. We investigate quantitatively the influence of task constraints and types of reinforcement signals on performance, diversity, and specialization in these collaborative experiments. Results show that, though diversity is not explicitly rewarded in our learning algorithm, even in scenarios without explicit communication among agents the swarm becomes specialized after learning. The degrees of both diversity and specialization are affected strongly by environmental conditions and task constraints. While the specialization measure reveals characteristics related to performance and learning in a clearer way than diversity does, the latter measure appears to be less sensitive to different noise conditions and learning parameters

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 Community (IST-2000-31010). The paper illustrates the robot hardware, and the results of experimental works in which distributed adaptive controllers are designed to allow the agents to perform a variety of tasks which require cooperation and coordination among the members of a group. 1

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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. In the multi-foraging task studied in this paper, a group of robots has to efficiently retrieve two different types of prey to a nest. Robots have to decide when they leave the nest to forage and which prey to retrieve. The goal of this study is to identify an efficient multi-foraging behaviour, where efficiency is defined as a function of the energy that is spent by the robots during exploration and gained when a prey is retrieved to the nest. We design and validate a mathematical model that is used to predict the optimal behaviour. We introduce a decision algorithm and use simulations to study its performance in a wide range of experimental situations with respect to the predictions of the mathematical model. Key words: swarm robotics, multi-foraging, mathematical modelling. 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.

Abstract. This paper addresses the issue of applying decentralised task allocation and task switching mechanisms in heterogeneous groups of robots in order to increase their ability to respond to task demand effectively. Our work is strongly inspired by the behaviour of eusocial insects (typically ants) and their behaviour of switching tasks in order to meet the changing demand. The objective of this paper is threefold: 1) identification of task allocation and task switching mechanisms in robots inspired by ants like red harvester ants, Pogonomyrmex barbatas, 2) developing a simple model of these mechanisms for use with a heterogeneous group of simulated robots and 3) implementing a decentralised and adaptive mechanism of updating thresholds for heterogeneous groups of simulated robots. The paper extends the use of threshold based mechanisms in homogeneous robots into the realms of heterogeneous groups of robots. Experimental results show that the incorporation of task switching mechanisms in specialised groups of robots improves the foraging efficiency and swarm energy significantly.