Towards a Swarm Robotic Approach for Cooperative Object Recognition
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Towards a Swarm Robotic Approach for Cooperative Object Recognition David King A thesis submitted in partial fulfilment of the requirements of Nottingham Trent University for the degree of Doctor of Philosophy September 2012 Copyright Notice This work is the intellectual property of the author. You may copy up to 5% of this work for private study, or personal, non-commercial research. Any re-use of the information contained within this document should be fully referenced, quoting the author, title, university, degree level and pagination. Queries or requests for any other use, or if a more substantial copy is required, should be directed in the owner(s) of the Intellectual Property Rights. ii Dedicated to the memory of Jean C. King, Mum. iii Acknowledgements It would not have possible for me to complete this thesis without the help and support of those around me. First of all my thanks go to my closest friends and family who have all been there at different times, in different ways. A special thanks to Charlotte Neep for listening to me talk for hours about all my ideas, thoughts and problems and to my Dad for his assistance in the final hours. My respect and admiration go to my supervision team who have given me the freedom to follow the research that I wanted whilst giving me the guidance and pressure I required to keep on track. More specifically I am indebted to; Dr Philip Breedon, my Director of Studies, who always made himself available for countless discussions about the direction my research was going, as well as guiding me through the whole process; Prof Wayne Cranton, my second supervisor, whose questioning made me think critically about my thesis; and Dr Sarah Kettley and Ms Phillipa Marsh, who both took the role of my third supervisor at different times, for providing a different perspective on my research. I also thank Dr Mark Norman who gave critical guidance when it was needed most as well as technical support with my first steps into multi-agent programming and for this I am truly thankful. My gratitude goes to Nottingham Trent University for its financial, academic and technical support and to its staff in the research office who helped me with endless queries and requests. Without the help of the Vice Chancellor‟s Bursary I would not have been able to support myself through the hours of work it took to complete my thesis. The opportunity provided by the University to teach and work with undergraduates has not only strengthened my abilities as an academic but also as kept me going when the bursary finally ran out. Finally I would like to thank all my fellow postgraduate researchers who I have met over the years that I have been working on my thesis. They have provided me with many opportunities to discuss problems and made me feel part of something. From this group special acknowledgements go to; Guy Birkin without whom I would not have got so far in my research; Mathew Malpass for been there in the early years and giving me guidance and many Friday afternoons when they were needed; Julian Robinson for lunch time conversations; Alice Dallabona for drinks, cakes and company; and Rebecca Gamble for encouraging comments when the research process got the better of me. iv Abstract Social insects have inspired the behaviours of swarm robotic systems for the last 20 years. Interactions of the simple individuals in these swarms form solutions to relatively complex problems. A novel swarm robotic method is investigated for future robotic cooperative object recognition tasks. Previous multi-agent systems involve cameras and image analyses to identify objects. They cooperate only to improve their hypotheses of the shape‟s identity. The system proposed uses agents whose interactions with each other around the physical boundaries of the object‟s shape allow the distinguishing features found. The agents are a physical embodiment of the vision system, making them suitable for environments where it would not be possible to use a camera. A Simplified Hexagonal Model was developed to simulate and examine the strategies. The hexagonal cells of which can be empty, contain an agent (hBot) or part of an object shape. Initially the hBots are required to identify the valid object shapes from a set of two types of known shapes. To do this the hBots change state when in contact with an object and when touching other hBots of the same state level, where some states are only achieved when neighbouring certain object shapes. The agents are oblivious, anonymous and homogeneous. They also do not know their position or orientation and cannot distinguish between object shapes alone due to their limited sensor range. Further work increased the number of object shapes to provide a range of scenarios. In order to hypothesise the difficulty a swarm of hBots has distinguishing one object shape type from any other a system is devised to compare object shapes. Data-chains describe the object shapes, without orientation, by considering how many object cells the empty cells surrounding them are in contact with. Pairs of object shapes could then be analysed to determine their difference value from each other. These difference values correlate to a swarms difficulty in completing the specific scenarios. Finally, a genetic algorithm (GA) was analysed as a method to determine the behaviours of the hBots different states. The GA is more efficient than both derived and randomly populated methods, showing that a GA can be used to train agents without first determining differences between the object shapes. These insights provide a significant contribution to knowledge through the object shape analyses method and the swarm robotic strategies which establish a unique foundation for further development of novel applications for both swarm robotic and cooperative object recognition research. v Glossary of Terms GA: Genetic Algorithm SHM: Simplified Hexagonal Model. The simulation model used to run the experiments for this thesis. Its basic design is a hexagonal arena consisting of hexagonal cells, which contain, either a hBot or object cell, or are considered empty. hBot: Hexagonal agent used in the Simplified Hexagonal Model Object Cells: Cells that are bound together to form an object shape. Object Shape: A shape made from bound object cells for use in the Simplified Hexagonal Model. To be considered an object shape, the grouping of object cells must be solid. The smallest object shape is a single cell. State: Describes the current state of a hBot, which is a reflection of its observations. State-Behaviour: The behaviour or actions a hBot will take at any given state. State-Level: The level which any given state is at. This gives an indication of how many hBots would be required to gain a specific state. At state-level 1, state-level 2 and state- level 3, it requires 1 hBot, 3 hBots and 5 hBots respectively. Data-Chain: An array of numerical values representing an object shape without considering position or placement by describing the number of object cells in contact with each of the empty cells surrounding the object shape in clock-wise order. The data- chain is written as an array but has no beginning nor end. Sub-Chain: A sub section of the data-chain of any given length. Difference Value: A value between 0 and 1 which measures the perceived difference between two object shapes considering all the sub-chains at a specific length for a given data-chain. vi Contents Copyright Notice ......................................................................................................................... ii Acknowledgements .................................................................................................................... iv Abstract ......................................................................................................................................... v Glossary of Terms ...................................................................................................................... vi Chapter 1: Introduction ........................................................................ 1 1.1 Background to Swarm Robotics ..................................................................................... 2 1.2 Research Questions .......................................................................................................... 3 1.3 Summary of the Investigation ......................................................................................... 4 1.4 Contributions to Knowledge .......................................................................................... 8 1.5 Outline ................................................................................................................................ 8 Chapter 2: Multi-Agent Systems ........................................................ 10 2.1 A Comparison of Methods ........................................................................................... 10 2.1.1 Centralised and Decentralised Control ............................................................................... 12 2.1.2 Communication ...................................................................................................................... 14 2.1.3 Identity and Anonymity ........................................................................................................ 16 2.1.4 Homogeneity .......................................................................................................................... 17 2.1.5 Recall and Computation ......................................................................................................