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This thesis deals with the design characteristics of a pipe crawling vehicle which utilises a unique, innovative and patented drive system. The principle of the drive system is simple. That is, if a brush is inserted into a pipe and its bristles are swept back at an angle, then, it is easier to push the brush forwards through the pipe than it is to pull it backwards. Thus, if two brushes are interconnected by a reciprocating cylinder, then, by cycling the cylinder, it is possible for the vehicle to "crawl" through the pipe. The drive mechanism has two main advantages. The first is the ability of the bristles to deflect over or around obstacles, thus, the vehicles can be used in severely damaged pipes. Secondly, the drive mechanism is able to generate extremely high "grip" forces, thus, the vehicle has a high payload to weight ratio. This "simple" traction mechanism has subsequently been proven to be extremely capable in significantly hostile environments, for example, nuclear plants and sewers. The development of the vehicle has resulted in brushes being considered as "engineering" components. This thesis considers the forces present when a brush moves forward through a pipe, further, it also considers the forces present if the brush is required to grip the walls of the pipe. A "simple" cantilever model has been developed which predicts the force required to push a brush forwards through the pipe. A second model has been developed which predicts the forward to reverse or "slip" to "grip" ratio of a brush, for given functional conditions. This model is deemed satisfactory up to the onset of bristle buckling. The experimental program determined three factors, they were, the force required to load a brush into a pipe, the force required to push a brush forward through a pipe and the reverse force a brush could support prior to failure. It can be concluded that this vehicle, through its tractive capability arid environmental compliance, is able to traverse irregularly shaped pipes. Ultimately, this allows tooling to be transported and used at previously unobtainable positions within such pipes.

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Mobile robot localisation has been, and continues to be, a very active research area. Estimating the position of a mobile robot is fundamental for its navigation and map-building. This thesis addresses some of the problems associated with mobile robot localisation. Three distinct items of research presented in this thesis are (i) A systematic odometry error model for a synchronous drive robot; and (ii) A novel method to synchronise two independent sensor data streams, and (iii) A proposal for an exteroceptive truly odometric sensor system - 'Visiodometry'. Cyclops is a synchronous drive mobile robot. The kinematics causes the path of the robot to curve, with the degree of curvature affected by the orientation of the wheels. A systematic odometry error model is proposed to correct for this. The proposed model is supported both experimentally, and theoretically from modelling the kinematics. Combining sensor data from different sensor data streams is commonly done to improve the accuracy of estimated variables. However, in some cases the sensors are not networked making it impossible to synchronise the data streams. The second item of research proposes a novel method to estimate the time difference in the local clocks of the discrete sensor data from their time-stamps alone. A proposed enhancement to the method improves both the rate of convergence and the precision of the estimate. Results show that the method is more optimum and robust than one based on known methods, including those based on Gaussian assumptions. Wheel odometry is a common method for mobile robot localisation. However, wheel odometry is unreliable if there is wheel slip. In these environments visual odometry has been used. However, the method does not work well on planar surfaces or surfaces with fine texture. It is also unable to accurately detect small motions less than a few centimetres. The third area of research proposes an exteroceptive odometric sensor called 'visiodometry' which is independent of the kinematics and therefore robust to wheel odometry errors. Two methods are proposed (i) a dual camera 'shift vector' method and (ii) a monocular 'roto-translation' method. The results demonstrate that the proposed system can provide odometric localisation data in planar environments to a high precision. The method is based upon extracting global motion estimates of affine transformed images of the ground using the phase correlation method. Experimental results demonstrate that, as a proof-of-concept, this type of sensor input is an alternative genuinely odometric input which has the potential to be comparable in accuracy and precision to wheel odometry in environments where wheel odometry and visual odometry methods are unreliable.

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In this thesis we focus on computer vision capabilities suitable for practical mass-market mobile robots, with an emphasis on techniques using rigs of multiple standard cameras rather than more specialised sensors. We analyse the state of the art of service robotics, and attempt to distill the vision capabilities which will be required of mobile robots over the mid and long-term future to permit autonomous localisation, mapping and navigation while integrating with other task-based vision requirements. The first main novel contribution of the work is to consider how an ad-hoc multi-camera rig can be used as the basis for metric navigation competences such as feature-based Simultaneous Localisation and Mapping (SLAM). The key requirement for the use of such techniques with multiple cameras is accurate calibration of the locations of the cameras as mounted on the robot. This is a challenging problem, since we consider the general case where the cameras might be mounted all around the robot with arbitrary 3D locations and orientations, and may have fields of view which do not intersect. In the second main part of the thesis, we move away from the idea that all cameras should contribute in a uniform manner to a single consistent metric representation, inspired by recent work on SLAM systems which have demonstrated impressive performance by a combination of off-the-shelf or simple techniques which we generally categorise by the term ‘lightweight’. We develop a multi-camera mobile robot vision system which goes beyond pure localisation and SLAM to permit fully autonomous mapping navigation within a cluttered room, requiring free-space mapping and obstacle-avoiding planning capabilities. In the last part of the work we investigate the trade-offs involved in defining a camera rig suitable for this type of vision system and perform some experiments on camera placement.

In recent years, there has been a considerable growth in applications of multi-robot systems as opposed to single-robot systems. This thesis presents our proposed solutions to a formation control problem in which mobile robots are required to create a desired formation shape and track a desired trajectory as a whole. In the first instance, we study the formation control problem for unicycle mobile robots. We propose two control algorithms based on a cascaded approach: one based on a kinematic model of a robot and the other based on a dynamic model. We also propose a saturated controller in which actuator limitations are explicitly accounted for. To demonstrate how the control algorithms work, we present an extensive simulation and experimental study. Thereafter we move on to formation control algorithms in which the coordination error is explicitly defined. Thus, we are able to give conditions for robots keeping their desired formation shape without necessarily tracking the desired trajectory. We also introduce a controller in which both trajectory tracking and formation shape maintenance are achieved as well as a saturated algorithm. We validate the applicability of the introduced controllers in simulations and experiments. Lastly, we study the formation control problem for car-like robots. In this case we develop a controller using the backstepping technique. We give conditions for robots keeping their desired formation shape while failing to track their desired trajectories and present simulation results to demonstrate the applicability of the proposed controller.

In the field of robotics, there has recently been tremendous progress in the development of autonomous robots that offer various services to their users. Most of the systems developed so far, however, are restricted to indoor scenarios, non-urban outdoor environments, or road usage with cars. There is a serious lack of capabilities of mobile robots to navigate safely in highly populated outdoor environments. This ability, however, is a key competence for a series of robotic applications. We consider the task of developing a spatially motivated dialogue system that can operate on a robotic platform, where the purpose of such a robot is to aid pedestrians in urban environments to provide information about surrounding objects and services, and guide users to desired destinations. In this thesis, we make a number of contributions to the fields of spatial language interpretation/generation and discourse modelling. This includes the development of a dialogue framework called HURDLE which builds on the strengths of existing systems, accompanied by a specific implementation for spatially oriented dialogue including disambiguating amongst objects and locations in the environment, and a natural language parser which combines an extension of Synchronous Context Free Grammars with a Part-of-Speech tagger. Our research also presents a number of probabilistic models for spatial prepositions such as `in front of' and `between' that make significant advances in effectively utilising geometric environment data, encompassing visibility considerations and being reusable for both indoor and outdoor environments. We also present a number of algorithms in which these models can be utilised, most significantly a novel and highly effective algorithm that can generate natural language descriptions of objects that disambiguates on their location. All these components, while modular, operate in tandem and interact with a variety of external components (such as path planning) on the robot platform.