Position and orientation errors in mobile robot absolute self-localization using an improved version of the generalized geometric triangulation algorithm

Triangulation with active beacons is widely used in the absolute localization of mobile robots. The original Generalized Geometric Triangulation algorithm suffers only from the restrictions that are common to all algorithms that perform self-localization through triangulation. But it is unable to compute position and orientation when the robot is over the segment of the line that goes by beacons 1 and 2 whose origin is beacon 1 and does not contain beacon 2. An improved version of the algorithm allows self-localization even when the robot is over that line segment. Simulations results suggest that a robot is able to localize itself, with small position and orientation errors, over a wide region of the plane, if measurement uncertainty is small enough

A New Three Object Triangulation Algorithm for Mobile Robot Positioning

Positioning is a fundamental issue in mobile robot applications. It can be achieved in many ways. Among them, triangulation based on angles measured with the help of beacons is a proven technique. Most of the many triangulation algorithms proposed so far have major limitations. For example, some of them need a particular beacon ordering, have blind spots, or only work within the triangle defined by the three beacons. More reliable methods exist; however, they have an increasing complexity or they require to handle certain spatial arrangements separately. In this paper, we present a simple and new three object triangulation algorithm, named ToTal, that natively works in the whole plane, and for any beacon ordering. We also provide a comprehensive comparison between many algorithms, and show that our algorithm is faster and simpler than comparable algorithms. In addition to its inherent efficiency, our algorithm provides a very useful and unique reliability measure, assessable anywhere in the plane, which can be used to identify pathological cases, or as a validation gate in Kalman filters.Peer reviewe

Mobile robot localization using a Kalman filter and relative bearing measurements to known landmarks

This paper discusses mobile robot localization using a single, fixed camera that is capable of detecting predefined landmarks in the environment. For each visible landmark, the camera provides a relative bearing but not a relative range. This research represents work toward an inexpensive sensor that could be added to a mobile robot in order to provide more accurate estimates of the robot\u27s location. It uses the Kalman filter as a framework, which is a proven method for incorporating sensor data into navigation problems. In the simulations presented later, it is assumed that the filter can perform accurate feature recognition. In the experimental setup, however, a webcam and an open source library are used to recognize and track bearing to a set of unique markers. Although this research requires that the landmark locations be known, in contrast to research in simultaneous localization and mapping, the results are still useful in an industrial setting where placing known landmarks would be acceptable

Identification and control of deposition processes

The electrochemical deposition process is defined as the production of a coating on a surface from an aqueous solution composed of several substances. Electrochemical deposition processes are characterized by strong nonlinearity, large complexity and disturbances. Therefore, improving production quality requires the identification of a reasonably accurate model which should be found from data in a reasonable amount of time and with a reasonable computational effort. This identification makes it possible to predict the behavior of unmeasured signals and design a control algorithm to meet the demands of consumers. This thesis addresses the identification and control of the deposition processes. A model for an electrochemical cell that takes into account both electrode interfaces and the activity of ions participating in the deposition process is developed and a method for taking into account uncompensated resistance is proposed. Identifiability of two models, the conventional model and the developed model, is investigated under step and sweep form of applied voltage. It is proven that conventional electrochemical cell model can be identified uniquely using a series of step voltage experiments or in a single linear sweep voltammetry experiment on the basis of the measurements of cell current. The Zakai filtering and pathwise filtering methods are applied to a nonlinear in the parameters electrochemical cell model to estimate the electrode kinetics and mass-transfer parameters of the copper electrodeposition process. In the case of known parameters the feedforward controllers that force the concentration at the boundary to follow the desired reference concentration are designed for the deposition processes. The adaptive boundary concentration control problem for the electrochemical cell with simultaneous parameter identification is solved using the Zakai filtering method. Using such a control, depletion in industrial applications, such as copper deposition baths, can be avoided. An identification method for identifying kinetic parameters and a time-varying mixed potential process of the nonlinear electroless nickel plating model is proposed. The method converts the original nonlinear time-varying identification problem into a time-invariant quadratic optimization problem solvable by conventional least squares

Information-theoretic environment modeling for mobile robot localization

To enhance robotic computational efficiency without degenerating accuracy, it is imperative to fit the right and exact amount of information in its simplest form to the investigated task. This thesis conforms to this reasoning in environment model building and robot localization. It puts forth an approach towards building maps and localizing a mobile robot efficiently with respect to unknown, unstructured and moderately dynamic environments. For this, the environment is modeled on an information-theoretic basis, more specifically in terms of its transmission property. Subsequently, the presented environment model, which does not specifically adhere to classical geometric modeling, succeeds in solving the environment disambiguation effectively. The proposed solution lays out a two-level hierarchical structure for localization. The structure makes use of extracted features, which are stored in two different resolutions in a single hybrid feature-map. This enables dual coarse-topological and fine-geometric localization modalities. The first level in the hierarchy describes the environment topologically, where a defined set of places is described by a probabilistic feature representation. A conditional entropy-based criterion is proposed to quantify the transinformation between the feature and the place domains. This criterion provides a double benefit of pruning the large dimensional feature space, and at the same time selecting the best discriminative features that overcome environment aliasing problems. Features with the highest transinformation are filtered and compressed to form a coarse resolution feature-map (codebook). Localization at this level is conducted through place matching. In the second level of the hierarchy, the map is viewed in high-resolution, as consisting of non-compressed entropy-processed features. These features are additionally tagged with their position information. Given the identified topological place provided by the first level, fine localization corresponding to the second level is executed using feature triangulation. To enhance the triangulation accuracy, redundant features are used and two metric evaluating criteria are employ-ed; one for dynamic features and mismatches detection, and another for feature selection. The proposed approach and methods have been tested in realistic indoor environments using a vision sensor and the Scale Invariant Feature Transform local feature extraction. Through experiments, it is demonstrated that an information-theoretic modeling approach is highly efficient in attaining combined accuracy and computational efficiency performances for localization. It has also been proven that the approach is capable of modeling environments with a high degree of unstructuredness, perceptual aliasing, and dynamic variations (illumination conditions; scene dynamics). The merit of employing this modeling type is that environment features are evaluated quantitatively, while at the same time qualitative conclusions are generated about feature selection and performance in a robot localization task. In this way, the accuracy of localization can be adapted in accordance with the available resources. The experimental results also show that the hybrid topological-metric map provides sufficient information to localize a mobile robot on two scales, independent of the robot motion model. The codebook exhibits fast and accurate topological localization at significant compression ratios. The hierarchical localization framework demonstrates robustness and optimized space and time complexities. This, in turn, provides scalability to large environments application and real-time employment adequacies