Robot soccer provides a fertile environment for the development of artificial intelligence techniques. Robot controls require high speed lower level reactive layers as well as higher level deliberative functions. This thesis focuses on a number of aspects in the robot soccer arena. Topics covered include boundary avoidance strategies, vision detection and the application of evolutionary learning to find fuzzy controllers for the control of mobile robot. A three input, two output controller using two angles and a distance as the input and producing two wheel velocity outputs, was developed using evolutionary learning. Current wheel velocities were excluded from the input. The controller produced was a coarse control permitting only either forward or reverse facing impact with the ball. A five input controller was developed which expanded upon the three input model by including the current wheel velocities as inputs. The controller allowed both forward and reverse facing impacts with the ball. A five input hierarchical three layer model was developed to reduce the number of rules to be learnt by an evolutionary algorithm. Its performance was the same as the five input model. Fuzzy clustering of evolved paths was limited by the information available from the paths. The information was sparse in many areas and did not produce a controller that could be used to control the robots. Research was also conducted on the derivation of simple obstacle avoidance strategies for robot soccer. A new decision region method for colour detection in the UV colour map to enable better detection of the robots using an overhead vision system. Experimental observations are given.

This thesis deals with the Simultaneous Localisation and Mapping algorithm as it pertains to the deployment of mobile systems in unknown environments. Simultaneous Localisation and Mapping (SLAM) as defined in this thesis is the process of concurrently building up a map of the environment and using this map to obtain improved estimates of the location of the vehicle. In essence, the vehicle relies on its ability to extract useful navigation information from the data returned by its sensors. The vehicle typically starts at an unknown location with no a priori knowledge of landmark locations. From relative observations of landmarks, it simultaneously computes an estimate of vehicle location and an estimate of landmark locations. While continuing in motion, the vehicle builds a complete map of landmarks and uses these to provide continuous estimates of the vehicle location. The potential for this type of navigation system for autonomous systems operating in unknown environments is enormous. One significant obstacle on the road to the implementation and deployment of large scale SLAM algorithms is the computational effort required to maintain the correlation information between features in the map and between the features and the vehicle. Performing the update of the covariance matrix is of O(n�) for a straightforward implementation of the Kalman Filter. In the case of the SLAM algorithm, this complexity can be reduced to O(n�) given the sparse nature of typical observations. Even so, this implies that the computational effort will grow with the square of the number of features maintained in the map. For maps containing more than a few tens of features, this computational burden will quickly make the update intractable - especially if the observation rates are high. An effective map-management technique is therefore required in order to help manage this complexity. The major contributions of this thesis arise from the formulation of a new approach to the mapping of terrain features that provides improved computational efficiency in the SLAM algorithm. Rather than incorporating every observation directly into the global map of the environment, the Constrained Local Submap Filter (CLSF) relies on creating an independent, local submap of the features in the immediate vicinity of the vehicle. This local submap is then periodically fused into the global map of the environment. This representation is shown to reduce the computational complexity of maintaining the global map estimates as well as improving the data association process by allowing the association decisions to be deferred until an improved local picture of the environment is available. This approach also lends itself well to three natural extensions to the representation that are also outlined in the thesis. These include the prospect of deploying multi-vehicle SLAM, the Constrained Relative Submap Filter and a novel feature initialisation technique. Results of this work are presented both in simulation and using real data collected during deployment of a submersible vehicle equipped with scanning sonar.

<p>Typically, when Faurecia in Torsås are planning a new assembly line, one of the steps is to build a full-scale model of the line in card board, a form of mock-up planning. Faurecia has shown interest in finding an alternative methodology. The purpose was to explore the possibility of replacing today's cardboard based mock-up planning with models in a digital simulation environment.</p><p>The purpose of the thesis has been to answer some of these complex questions; What are the benefits of using a simulation tool? In what way can we use it? If we choose to use a simulation tool, in what way does it affect our way of planning an assembly line? What are the advantages and disadvantages? Are there any economical and social aspects?</p><p>Our results made us realize that a digital simulation tool does not replace the card board. On the other hand simulation could be used as a complement. Card board exercises and simulations play two different roles in the development of an assembly line. The use of card board is an excellent example of how you can generate a wide range of creative solutions at an early stage of the design process. This offers the possibility to create and evaluate several different concepts during a short period of time.</p><p>We believe that the benefits of a simulation tool come in handy at a later stage of the design process, when a conceptual solution is selected. The simulation tool will be able to evaluate the solution and identify where problems may occur. The output of the simulation tool may also be used as a communication tool, internally as well as externally.</p>

<p>Autonomous robots typically rely on internal representations of the environment, or maps, to plan and execute their tasks. Several types of maps have been proposed in the literature, and there is general consensus that different types have different advantages and limitations, and that each type is more suited to certain tasks and less to others. Because of these reasons, it is becoming common wisdom in the field of mobile robotics to use hybrid maps that integrate several representations, usually of different types. Hybrid maps provide scalability and multiple views, allowing for instance to combine robot-centered and human-centered representations. There is, however, little understanding of the general principles that can be used to combine different maps into a hybrid one, and to make it something more than the sum of its parts. There is no systematic analysis of the different ways in which different maps can be combined, and how they can be made to cooperate. This makes it difficult to evaluate and compare different systems, and precludes us from getting a clear understanding of how a hybrid map can be designed or improved.</p><p>The investigation presented in this thesis aims to contribute to fill this foundational gap, and to get a clearer understanding of the nature of hybrid maps. To help in this investigation, we develop two tools: The first one is a conceptual tool, an analytical framework in which the main ingredients of a hybrid map are described; the second one is an empirical tool, a new hybrid map that allows us to experimentally verify our claims and hypotheses.</p><p>While these tools are themselves important contributions of this thesis, our investigation has resulted in the following additional outcomes:</p><p>• A set of concepts that allow us to better understand the structure and operation of hybrid maps, and that help us to design them, compare them, identify their problems, and possibly improve them;</p><p>• The identification of the notion of synergy as the fundamental way in which component maps inside a hybrid map cooperate.</p><p>To assess the significance of these outcomes, we make and validate the following claims:</p><p>1. Our framework allows us to classify and describe existing maps in a uniform way. This claim is validated constructively by making a thorough classification of the hybrid maps reported in the literature.</p><p>2. Our framework also allows us to enhance an existing hybrid map by identifying spots for improvement. This claim is verified experimentally by modifying an existing map and evaluating its performance against the original one.</p><p>3. The notion of synergy plays an important role in hybrid maps. This claim is verified experimentally by testing the performance of a hybrid map with and without synergy.</p>

We report on experiments with a mobile robot using one vision process (forward motion vision) to calibrate another (stereo vision) without resorting to any external units of measurement. Both are calibrated to a velocity dependent coordinate system which is natural to the task of obstacle avoidance. The foundations of these algorithms, in a world of perfect measurement, are quite elementary. The contribution of this work is to make them noise tolerant while remaining simple computationally. Both the algorithms and the calibration procedure are easy to implement and have shallow computational depth, making them (1) run at reasonable speed on moderate uni-processors, (2) appear practical to run continuously, maintaining an up-to-the-second calibration on a mobile robot, and (3) appear to be good candidates for massively parallel implementations.

A mobile robot must be able to determine its own position in the world. To support truly autonomous navigation, we present a system that builds and maintains its own models of world locations and uses these models to recognize its world position from stereo vision input. The system is designed to be robust with respect to input errors and to respond to a gradually changing world by updating the world location models. We present results from tests of the system that demonstrate its reliability. The model builder and recognition system fit into a planned world modeling system that we describe.

Representing the world in terms of visible surfaces and the freespacesexisting between these surfaces and the viewer is an important problemsin robotics. Recently, researchers have proposed using the 3DsDelaunay Tessellation for representing 3D stereo vision data and thesfreespace determined therefrom. We discuss problems with using thes3D Delaunay Tessellation as the basis of the representation andspropose an alternative representation that we are currentlysinvestigating. This new representation is appropriate for planningsmobile robot navigation and promises to be robust when using stereosdata that has errors and uncertainty.

The goal of this research is to develop the prototype of a tactile sensing platform for anthropomorphic manipulation research. We investigate this problem through the fabrication and simple control of a planar 2-DOF robotic finger inspired by anatomic consistency, self-containment, and adaptability. The robot is equipped with a tactile sensor array based on optical transducer technology whereby localized changes in light intensity within an illuminated foam substrate correspond to the distribution and magnitude of forces applied to the sensor surface plane. The integration of tactile perception is a key component in realizing robotic systems which organically interact with the world. Such natural behavior is characterized by compliant performance that can initiate internal, and respond to external, force application in a dynamic environment. However, most of the current manipulators that support some form of haptic feedback either solely derive proprioceptive sensation or only limit tactile sensors to the mechanical fingertips. These constraints are due to the technological challenges involved in high resolution, multi-point tactile perception. In this work, however, we take the opposite approach, emphasizing the role of full-finger tactile feedback in the refinement of manual capabilities. To this end, we propose and implement a control framework for sensorimotor coordination analogous to infant-level grasping and fixturing reflexes. This thesis details the mechanisms used to achieve these sensory, actuation, and control objectives, along with the design philosophies and biological influences behind them. The results of behavioral experiments with a simple tactilely-modulated control scheme are also described. The hope is to integrate the modular finger into an %engineered analog of the human hand with a complete haptic system.

En esta tesis se desarrolla una arquitectura de control que involucra un CPG y dos pares controlador-identificador neurales para un robot planar bípedo, que es capaz de caminar en superficies planas sin conocer el modelo dinámico del robot, con este controlador se logra que un robot bípedo simulado camine dinámicamente por una superficie plana, con un caminar continuo y estable, la símilaridad biológica con el caminar humano es bastante aparente y el caminar se ve bastante natural, aunque lento. Se presentan también simulaciones sobre modelos comunes para el equilibrio humano, consiguiendo controlarlos exitosamente. también se desarrolla una extensión al caso de múltiples entradas y múltiples salidas del esquema de control con redes neurales diagonales recurrentes basado en modelos, demostrando que este nuevo controlador tiene la capacidad de efectuar tanto la cinemática inversa, como la dinámica inversa de un manipulador, cumpliendo objetivos de control que son medidas del rendimiento de la planta

En esta tesis se desarrolla una arquitectura de control que involucra un CPG y dos pares controlador-identificador neurales para un robot planar bípedo, que es capaz de caminar en superficies planas sin conocer el modelo dinámico del robot, con este controlador se logra que un robot bípedo simulado camine dinámicamente por una superficie plana, con un caminar continuo y estable, la símilaridad biológica con el caminar humano es bastante aparente y el caminar se ve bastante natural, aunque lento. Se presentan también simulaciones sobre modelos comunes para el equilibrio humano, consiguiendo controlarlos exitosamente. también se desarrolla una extensión al caso de múltiples entradas y múltiples salidas del esquema de control con redes neurales diagonales recurrentes basado en modelos, demostrando que este nuevo controlador tiene la capacidad de efectuar tanto la cinemática inversa, como la dinámica inversa de un manipulador, cumpliendo objetivos de control que son medidas del rendimiento de la planta