Coordination and control mechanisms for embedded swarm-like agents

Mullen, Robert (2011) Coordination and control mechanisms for embedded swarm-like agents. (PhD thesis), Kingston University, .


Observations of the mechanisms of natural systems have given us a wide-range of problem solving tools that can be applied to computational and technology related challenges. This thesis explores the use of swarm intelligence mechanisms to facilitate group level cooperative coordination and control of swarm-like agents that are embedded in 2D or 3D environments, and explores how distributed dynamic behaviours can be integrated in to the self-organisation process. Specifically a number of algorithms are developed to facilitate adaptive pattern formation and manipulation for two distinctly different problems. Firstly, large-scale pattern formation is considered using an embedded swarm of software agents. The agents are considered as virtual entities which are embedded into digital images at the pixel level, such that the intensity map of the image corresponds to a landscape within which the swarm of agents move. The agent-agent and agent-environment interactions are then studied in the context of emergent pattern formation, from which a number of ant-algorithms are developed to achieve a range of image and video processiug solutions by inducing swarm self-organisation in response to user specified image features. Artificial pheromones are used to reinforce features of interest in the image landscape, and after the swarm has self-organised, the resultant pheromone map reveals the pattern feature to be extracted. The algorithm can be adapted for different types of image features with relative ease, and simultaneous self-organisation of multiple swarms in the same image environment is implemented to achieve distributed multi-feature extraction. The dynamic nature of the self-organising process is exploited to extend the functionality of the algorithm to feature tracking in real-time imagery, where the swarms effectively track features of interest from frame to frame. An adaptive threshold method is developed which exploits the distributed nature of the swarm approach, by allowing individual ant agents to adapt their own feature threshold parameters in response to their local environment. This is both an interesting study with regards to artificial swarm pattern formation, and also provides practical image and video processing solutions which do not require a full image scan or any filtering operations, unlike many traditional methods. The novel adaptive threshold method eliminates the requirement for a user-set threshold, and allows for distributed, multi-level thresholding across image environments, as well as adaptive capabilities for dynamic imagery. The second problem focuses on pattern formation and manipulation of a small swarm of hardware agents in a swarm robotics problem setting. Transferring from software agents to hardware agents introduces several difficulties to overcome in order to fully realise the distributed nature of the swarm intelligence approach to multi-robot formation control. The second part of this thesis focuses on designing a control architecture that enables cooperative coordination and control of multiple robots, leading to group level adaptive pattern formation and manipulation, using a fully distributed algorithim that requires no inter-robot communication and retains robot anonymity. This is achieved using a distributed variation of the virtual forces approach. The use of a genetic algorithm for problem specific parameter optimisation is investigated to improve performance with respect to pattern formation for area coverage. A multi-behavioural approach is investigated for the problem scenario of locating and monitoring multiple target areas within a partially observable environment, where the self-organising pattern formation behaviours are exploited to provide distributed coverage. A new mechanism called Virtual Robot Nodes (VRNs) is introduced which improves swarm-level cohesion and allows for more complex formation and pattern management. The VRN method allows individual robots to self-manage their experieneed virtual forces in response to their perception of their local environment and neighbouring robots, allowing for distributed dynamic adaptation. Verification of the proposed algorithms is carried out through a range of experiments in 2D simulation, physics and sensor based simulation, and embedded simulation on real robots in a laboratory environment, for a range of test scenarios. The application of different nature inspired control architectures for small to large sized swarms, and from software entities to hardware entities, promotes a focal point for discussion on the wide-ranging potential for harnessing the knowledge of nature in solving computational problems.

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