Browsing by Author "Gerdelan, Anton"

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    Fuzzy motion controllers and hybrids : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, New Zealand
    (Massey University, 2011) Gerdelan, Anton; Gerdelan, Anton
    This thesis describes implementations of motion control systems that are based on fuzzy logic; fuzzy motion controllers. The controllers are used by to drive a variety of simulated vehicles and computeranimated characters. The problem of heading towards a destination whilst simultaneously avoiding static and dynamic obstacles is addressed with fuzzy motion controllers. For situations where a level above reactive motion control is required, such as path-planning behaviour or traffic rule following, then hybrid algorithms are proposed; combining fuzzy motion controllers with other navigation algorithms. Consideration is given to behavioural level of detail models, with transition between behavioural models of different complexity based on the proximity, or visual importance of characters to the camera. Fuzzy controllers have a set of fuzzy rules, or a “rule base” that defines the inference of the controller. There is no assurance that hand-calibrated rule bases are optimal, and indeed that calibration based on fixed test environment will apply well to a dynamic environment. Special consideration is given to genetic-fuzzy systems, which use a genetic algorithm to automatically calibrate a rule base. Various architectures for genetic-fuzzy system are proposed and evaluated including dynamic systems, which have the ability to learn “on the fly”, rather than in fixed experiment scenarios. A relationship between genetic algorithm parameters and time-efficient fitness improvement is found.The time requirements of training more complex “cascading” fuzzy systems are discussed. Distributed and parallel training models are also considered. A new, modular agent middleware is proposed, which is the underpinning software that perceives the complex environment, feeds inputs into the fuzzy motion controllers, and effects output actions for each character and vehicle. The middleware model is successfully used to drive a range of vehicles and characters used in experiments. The problem of evaluating motion controllers within a scientific framework is discussed. Several candidate solutions are used, and a system for objectively evaluating mechanically simulated vehicle motion is defined and evaluated. A complete tool-chain for designing complex simulations and doing scientific experiments with them is is developed and discussed in detail, including simulation software design methods, libraries, visualisation tools, and useful algorithms, a well-defined mechanical simulation system, and practices for collecting data from simulations, and quantifying uncertainty.