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This thesis describes a navigational strategy for automatically guided vehicles and a behavioural control implementation using a modified zero order Sugeno fuzzy inference engine. Animals are examined as a model of intelligent behaviour and behavioural control implementations discussed and compared. The features and the requirements of a behavioural control implementation are identified and modifications to Sugeno inferencing are then described which allow these to be met. A successful implementation is then presented. The navigation task is examined, as are various methods of representing space. Special structural features of constrained spaces are identified. A route based navigation system exploiting diese features is then developed. The strategy combines the representation of space with the task description into an easily communicated message. This navigational strategy is then successfully integrated with the behavioural control implementation presented earlier. The influence of spatial structure on the complexity of the navigation task is investigated with a view to the categorisation of space. A definition of maze-spaces is developed from this. Conclusions are then drawn on the themes of the work, and suggestions made for further investigations.

This thesis is concerned with the robots' motion planning problem. In particular it is focused on the path planning and motion planning for Autonomously Guided Vehicles (AGVs) in well-structured, two-dimensional static and dynamic environments. Two algorithms are proposed for solving the aforementioned problems. The first algorithm establishes the shortest collision-semi-free path for an AGV from its start point to its goal point, in a two-dimensional static environment populated by simple polygonal obstacles. This algorithm constructs and searches a reduced visibility graph, within the AGV's configuration space, using heuristic information about the problem domain. The second algorithm establishes the time minimal collision-semi-free motion for an AGV, from its start point to is goal point, in a two-dimensional dynamic environment populated by simple polygonal obstacles. This algorithm considers the AGV's spacetime configuration space, thus reducing the dynamic motion planning problem to the static path planning problem. A reduced visibility graph is then constructed and searched using information about the problem domain, in the AGV's space-time configuration space in order to establish the time-minimal motion between the AGV's start and goal configurations. The latter algorithm is extended to solve more complicated instances of the dynamic motion planning problem, where the AGV's environment is populated by obstacles, which change their size as well as their position over time and obstacles, which have piecewise linear motion. The proposed algorithms can be used to efficiently and safely navigate AGVs in well structured environments. For example, for the navigation of an AGV, in industrial environments, where it operates as part of the manufacturing process or in chemical and nuclear plants, where the hostile environment is inaccessible to humans. The main contributions in this thesis are, the systematic study of the V*GRAPH algorithm and identification of its methodic and algorithmic deficiencies; recommendation of corrections and further improvements on the V* GRAPH algorithm, which in turn lead to the proposition of the V*MECHA algorithm for robot path planning; proposition of the D*MECHA algorithm for motion planning in dynamic environments; extension to the D*MECHA algorithm to solve more complicated instances of the dynamic robot motion planning problem; discussion of formal proofs of the proposed algorithms' correctness and optimality and critical comparisons with existing similar algorithms for solving the motion planning problem.