Formation and organisation in robot swarms. OTHMAN, Wan Amir Fuad Wajdi. Available from the Sheffield Hallam University Research Archive (SHURA) at: http://shura.shu.ac.uk/20156/ A Sheffield Hallam University thesis This thesis is protected by copyright which belongs to the author. The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author. When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given. Please visit http://shura.shu.ac.uk/20156/ and http://shura.shu.ac.uk/information.html for further details about copyright and re-use permissions. 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Box 1346 Ann Arbor, Ml 4 8 1 0 6 - 1346 Formation and Organisation in Robot Swarms Wan Amir Fuad Wajdi Othman A thesis submitted in partial fulfilment of the requirements of Sheffield Hallam University for the degree of Doctor of Philosophy March 2009 Declaration This is to certify that I am responsible for the work submitted in the thesis, that the original work is my own except as specified in acknowledgements, and that neither the thesis nor the original work contained therein has been submitted to this or any institution for a higher degree. Signature: ........................................................................ Name : WAN AMIR FUAD WAJDI OTHMAN Date iO March 2009 Acknowledgement I would like to express my sincere gratitude to my research supervisor, Bala P. Amavasai, for his excellent guidance and consistent support during my research. I am grateful to him for giving me an invaluable opportunity to work on four different aspect of swarming over the past four years. Thanks for broadening my scope and helping me achieve my goals. I would like to communicate my appreciation to Jon R. Travis, Bala P. Amavasai and Reza Saatchi for their interest and enthusiasm, to read, modify and comment on the manuscript. I would like to thank all colleagues of the Microsystems and Machine Vision Laboratory, particularly Jan Wedekind, Kim Chuan Lim, Tan Kang Song, Manuel Boissenin and Arul Selvan for their support and encouragement. Also, I would like to extend my gratitude to Fabio Caparrelli and Stephen McKibbin for many insightful suggestions, fruitful discussions and constructive comments. I would like also to thank Alan Goude for his enthusiasm in teaching and guidance on my extra curriculum lecture. I benefited a lot from his valuable course on Object Oriented Programming. Thanks to the Ministry of Higher Education of Malaysia and University Science of Malaysia for giving me the opportunity to pursue my research, and for sponsoring my studies. Special thanks to members of the Registrar office of USM particularly to Fatimah Othman and Ramli Osman, for their kind support and cooperation towards the completion o f this thesis. Finally, but most importantly, I would like to express my sincere love and appreciation to my mother, Faizah Othman, my wife, Fazrin Mohamad Azemi and my son Aleef Imran for their unconditional love, support, patience, encouragement and du'a throughout the work which has taken many hours that should have been dedicated exclusively to them. No matter, what happens they have always stood behind me and have shared in my happiness and difficulties. Thanks again to my wife, she has tolerated all my stress, helping me with her unbelievable love. Also I would like to express my love to my late father, brothers and sisters as well as parents in law for their continuous support and du'a. Abstract A swarm is defined as a large and independent collection of heterogeneous or homogeneous agents operating in a common environment and seemingly acting in a coherent and coordinated manner. Swarm architectures promote decentralisation and self-organisation which often leads to emergent behaviour. The emergent behaviour of the swarm results from the interactions of the swarm with its environment (or fellow agents), but not as a direct result of design. The creation of artificially simulated swarms or practical robot swarms has become an interesting topic of research in the last decade. Even though many studies have been undertaken using a practical approach to swarm construction, there are still many problems need to be addressed. Such problems include the problem of how to control very simple agents to form patterns; the problem of how an attractor will affect flocking behaviour; and the problem of bridging formation of multiple agents in connecting multiple locations. The central goal of this thesis is to develop early novel theories and algorithms to support swarm robots in. pattern formation tasks. To achieve this, appropriate tools for understanding how to model, design and control individual units have to be developed. This thesis consists of three independent pieces of research work that address the problem of pattern formation of robot swarms in both a centralised and a decentralised way. The first research contribution proposes algorithms of line formation and cluster formation in a decentralised way for relatively simple homogenous agents with very little memory, limited sensing capabilities and processing power. This research utilises the Finite State Machine approach. In the second research contribution, by extending Wilensky's (1999) work on flocking, three different movement models are modelled by changing the maximum viewing angle each agent possesses during the course of changing its direction. An object which releases an artificial potential field is then introduced in the centre of the arena and the behaviours of the collective movement model are studied. The third research contribution studies the complex formation of agents in a task that requires a formation of agents between two locations. This novel research proposes the use Of L-Systems that are evolved using genetic algorithms so that more complex pattern formations can be represented and achieved. Agents will need to have the ability to interpret short strings of rules that form the basic DNA of the formation. v Table of Contents Chapter 1 Thesis Overview * ................................................................. 1 1.1 Motivation ..........................,................................. 1 1.2 Self Organisation ......................... 3 1.3 Context ......................................................................................................................................... 6 1.4 Structure of the Thesis... ..................................... 8 Chapter 2 Literature Survey ....................... 10 2.1 Pattern Formations ....................................................................................................................10 2.1.1 Pattern Forming Paradigms ...................................................................13 2.1.1.1 Biomimetics... ................................................................ 13 2.1.1.2 Physicomimetics..... ................................................................... 16 2.1.2 Organised Formations... ...................................................................:.......................19 2.1.2.1 Centralised ........................................... 21 2.1.2.2 Decentralised ............................... 23 Behaviour based approach .......................... 23 Leader-follower approach ....................... .29 2.2 Definitions .................................... 33 2.2.1 Intelligence ....................................................................... 33 2.2.2 Swarm Intelligence ....................... 35 2.3 Swarm Robotics ..................................... 39 2.3.1 The Autonomous Nano-Technology Swarm (ANTS) ............................... 42 2.3.2 The Swarm-bots project ........................................................................................... 42 2.3.3 The Pheromones robotics project .............................................. 43 2.3.4 The GUARDIANS project ................................................. 44 2.4 Robot Architecture ........................... 44 2.5 Artificial Life ................................................ .....47 2.5.1 Inspirations from Natural Systems ......................................................... 47 2.5.2 L-Systems ........... 52 2.6 Swarm Modelling ..................................................................................................................... 55 2.6.1 Eularian model .................. 55 2.6.2 Lagrangian model .................................................................. 56 2.6.3 Behaviour-based model ................... 56 2.7 Simulation Tools ................... 60 2.7.1 Breve .................. :.60 2.7.2 NetLogo .................................. 62 2.7.3