The kinematic mapping of a rigid open-link manipulator is a homomorphism between Lie groups. The homomorphisrn has solution groups that act on an inverse kinematic solution element. A canonical representation of solution group operators that act on a solution element of three and seven degree-of-freedom (do!) dextrous manipulators is determined by geometric analysis. Seven canonical solution groups are determined for the seven do! Robotics Research K-1207 and Hollerbach arms. The solution element of a dextrous manipulator is a collection of trivial fibre bundles with solution fibres homotopic to the Torus. If fibre solutions are parameterised by a scalar, a direct inverse funct.ion that maps the scalar and Cartesian base space coordinates to solution element fibre coordinates may be defined. A direct inverse pararneterisation of a solution element may be approximated by a local linear map generated by an inverse augmented Jacobian correction of a linear interpolation. The action of canonical solution group operators on a local linear approximation of the solution element of inverse kinematics of dextrous manipulators generates cyclical solutions. The solution representation is proposed as a model of inverse kinematic transformations in primate nervous systems. Simultaneous calibration of a composition of stereo-camera and manipulator kinematic models is under-determined by equi-output parameter groups in the composition of stereo-camera and Denavit Hartenberg (DH) rnodels. An error measure for simultaneous calibration of a composition of models is derived and parameter subsets with no equi-output groups are determined by numerical experiments to simultaneously calibrate the composition of homogeneous or pan-tilt stereo-camera with DH models. For acceleration of exact Newton second-order re-calibration of DH parameters after a sequential calibration of stereo-camera and DH parameters, an optimal numerical evaluation of DH matrix first order and second order error derivatives with respect to a re-calibration error function is derived, implemented and tested. A distributed object environment for point and click image-based tele-command of manipulators and stereo-cameras is specified and implemented that supports rapid prototyping of numerical experiments in distributed system control. The environment is validated by a hierarchical k-fold cross validated calibration to Cartesian space of a radial basis function regression correction of an affine stereo model. Basic design and performance requirements are defined for scalable virtual micro-kernels that broker inter-Java-virtual-machine remote method invocations between components of secure manageable fault-tolerant open distributed agile Total Quality Managed ISO 9000+ conformant Just in Time manufacturing systems.

This thesis describes the history of robots and explains the reasons for the international differences in robot '. diffusion, and the differences in the diffusion of various iobot applications with reference to the UK. As opposed to most of the literature, diffusion is examined with an integrated and interdisciplinary perspective. Robot technology evolves from the interaction of development, supply and manufacture, adoption, and promOtion. activities. Emphasis is given to the analysis of adoption, at present the most important limiting factor of robot advancement in the UK. Technical development is inferred from a comp'arison of surveys on equipment, and from the topics of ten years of symposia papers. This classification of papers is also used to highlight the international and institutional differences in robot development. Analysis of the growth in robot supply, manufacture, and use is made from statistics compiled. A series of interviews with users and potential users serves to illustrate the factors and implications of the adoption of different robot systems in the UK. Adoption pioneering takes place when several conditions exist: when the technology is compatible with the firm, when its advantages outweigh its disadvantages, and particularly when a climate exists which encourages the managerial involvement and the labour acceptance. The degree of compatibility (technical, methodological, organisational, and economic) and the consequences (profitability, labour impacts, and managerial effects) of different robot systems (transfer, manipulative, processing, and assembly) are determined by various aspects of manufacturing operations (complexity, automation, integration, labour tasks, and working conditions). The climate for adoption pioneering is basically determined by the performance of firms. The firms' policies on capital investment have as decisive a role in determining the profitability of robots as their total labour costs. The performance of the motor car industry and its machine builders explains, more than any other factor, the present state of robot advancement in the UK.

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Je présente une approche cognitive pour la planification de trajectoire sous contraintes. Elle repose en premier lieu sur DKP : une approche de planification de trajectoire par échantillonage. DKP construit un arbre d’exploration dans les parties atteignables de l’environnement à la manière d’un A*. Les solutions sont des trajectoires splines adaptées au contrôle de robots à deux roues indépendantes. La couche de propagation construit un espace des paramètres, contenant toutes les valeurs des paramètres laissés libres dans les solutions. Il est représenté sous la forme d’une surface contenant toutes les solutions locales du problème qui respectent les contraintes du problème : vitesse, accélération, évitement d’obstacle,. . .Ensuite, une recherche de solutions est effectuée sur l’espace des paramètres selon un critère de recherche. DKP a la propriété d’êtredéterministe : les résultats sont reproductibles et son comportement est entièrement contrôlable. Ce contrôle me sert à définir des comportements de pilotage. Ils sont exprimés sous la forme d’un arbre de comportements : chaque comportement agit sur la manière dont l’arbre d’exploration progresse dans l’environnement. Les comportements sont appliqués en fonction de la partie explorée. Ainsi, seuls les comportements faisables sont développés. TÆMS permet de décrire ces comportements de pilotage puis d’évaluer les solutions ainsi construites par DKP. Au final, mon approche cognitive repose sur l’évolution conjointe d’un arbre de comportements de pilotage et d’un arbre d’exploration : elle fait ainsi le lien entre planification classique et planification de trajectoire sous contraintes. / In this thesis, I present two new contributions. First, I provide DKP : a sample-based approach for trajectory planning.DKP uses a selection/propagation architecture to build an exploration tree in the reachable part of the environment, guided in an A∗ manner. Solutions are spline trajectories that are immediately executable by two-wheeled robots. The propagation level builds a parameter space which contains all the values of the free parameters in the solution. It is represented as a surface containing all local solutions which respect kinodynamic constraints : speed, acceleration, obstacle avoidance,. . .Then, a search is applied on the parameter space using a search criterion. DKP is deterministic : every result produced by DKP may be repeated. Second, this control is used to define steering behaviors. These are expressed within a steering tree : every behavior acts on the way the exploration tree progresses in the environment. Steering behaviors are applied according to the explored part. Thereby, TÆMS is used to describe the steering behaviors and to evaluate the solutions provided by DKP. Finally, my cognitive approach takes advantage on the common building of a steering tree and an exploration tree which validates respect of constraints : we thus get a link between classing planning and trajectory planning under constraints.

Truly autonomous operation for any field robot relies on a well-defined pyramid of technical competencies. Consider the case of an autonomous car – we require the vehicle to be able to perceive its environment through noisy sensors, robustly fuse this information into an accurate representation of the world, and use this representation to plan and execute complex tasks – all the while dealing with the uncertainties inherent in real world operation. Of fundamental importance to all these capabilities is localisation – we always need to know where we are, if we are to be able to plan where we are going (or how to get there). As road vehicles make the push towards becoming truly autonomous, the system’s ability to stay accurately localised over its operating lifetime is of crucial importance – this is the core issue of lifelong localisation. The goals in this thesis are threefold – to develop the hardware needed to reliably acquire data over large scales, to build a localisation framework that is robust enough to be used over the long–term, and to establish a method of adapting our framework when necessary such that we can accommodate the inevitable difficulties present when operating over city-scales. We begin by developing the physical means to make large-scale localisation achievable, and affordable. This takes the form of a stand-alone, rugged sensor payload – incorporating a number of sensing modalities – that can be deployed in either a mapping or localisation role. We then present a new technique for localisation in a prior map using an information theoretic framework. The core idea is to build a dense retrospective sensor history, which is then matched statistically within a prior map. The underlying idea is to leverage the persistent structure in the environment, and we show that by doing so, it is possible to stay localised over the course of many months and kilometres. The developed system relies on orthogonally-oriented ranging sensors, to infer both velocity and pose. However, operating in a complex, dynamic, setting (like a town centre) can often induce velocity errors, distorting our sensor history and resulting in localisation failure. The insight into dealing with this failure is to learn from sensor context – we learn a place-dependent sensor model and show that doing so is vital to prevent such failures. The integration of these three competencies gives us the means to make inex- pensive, lifelong localisation an achievable goal.

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Shared control is a technique to provide interactive autonomy in a telerobotic task, replacing the requirement for pure teleoperation where the operator's intervention is unnecessary or even undesirable. In this thesis, a geometrically correct theory of shared control for teleoperation is developed using differential geometry. The autonomous function proposed is force control. In shared control, the workspace is commonly partitioned into a "position domain" and a "force domain". This computational process requires the use of a metric. In the context of manifolds, these are known as Riemannian metrics. The switching matrix is shown to be equivalent to a filter which embodies a Riemannian metric form. However, since the metric form is non-invariant, it is shown that the metric form must undergo a transformation if the measurement reference frame is moved. If the transformation is not made, then the switching matrix fails to produce correct results in the new measurement frame. Alternatively, the switching matrix can be viewed as a misinterpretation of a projection operator. Again, the projection operator needs to be transformed correctly if the measurement reference frame is moved. Many robot control architectures preclude the implementation of robust force control. However, a compliant device mounted between the robot wrist and the workpiece can be a good alternative in lieu of explicit force control. In this form of shared control, force and displacement are regulated by control of displacement only. The geometry of compliant devices is examined in the context of shared control and a geometrically correct scheme for shared control is derived. This scheme follows naturally from a theoretical analysis of stiffness and potential energy. This thesis unifies some recent results formulated for robotic hybrid position / force control under the modern framework of differential geometry and Lie groups.

By "intelligently" locating a sensor with respect to its environment it is possible to minimize the number of sensing operations required to perform many tasks. This is particularly important for sensing media which provide only "sparse" data, such as tactile sensors and sonar. In this thesis, a system is described which uses the principles of statistical decision theory to determine the optimal sensing locations to perform recognition and localization operations. The system uses a Bayesian approach to utilize any prior object information (including object models or previously-acquired sensory data) in choosing the sensing locations.

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