This paper introduces a new method for the pattern-wise anomaly detection problem, which aims to find segments whose behaviors are different from the rest of the segments in the time series (as opposed to finding a single data-point in classic anomaly detection problems). An important motivation for studying this problem is to find anomalies whose data-points are within the normal range but they create an unusual pattern. To this end, normal characteristics of the data are found by clustering the overlapping subsequences of the training dataset and analyzing their orders by Markov chains. The trained model is used to assess how well the testing dataset suits the baseline behavior. The designed anomaly detection framework is capable of discovering unusual patterns in both streaming data (online) and stored data (offline). The performance of the methodology is evaluated by applying it to three datasets from different fields: a medical dataset (electrocardiogram), a utility usage dataset, and a New York City taxi demand dataset. The detected anomaly in the medical data agrees with the results of the studies in the literature. A domain expert confirmed the accuracy of the results for the utility usage data, and the anomalies of the New York City taxi demand data referred to major US holidays.

Hybridizing evolutionary algorithms with local search has been quite successful. EAs are designed to exponentially exploit promising regions of the search space; however, by doing so may waste computation effort by duplicating local searches. Therefore, an adaptive memory method is used to determine when to apply a local search.

We fuse a self-organizing hyperellipsoid clustering (HEC) Kohonen neural network with the Julier-Uhlmann-Kalman filter (JUKF) to perform map building and low-level position estimation. The HEC Kohonen uses the Mahalanobis distance to learn elongated shapes (typical of sonar data) and obtain a stochastic measurement of data-node association. The number of nodes is regulated by measuring how well a node model matches its associated data. The HEC Kohonen can handle high-dimensional problems and can be generalized to other pattern recognition problems. The JUKF compliments the HEC Kohonen in that it performs low-level (nonlinear) tracking more efficiently and more accurately than the extended Kalman filter. By estimating and propagating error covariances through system transformations, the JUKF eliminates the need to derive Jacobian matrices. The inclusion of stochastic information inherent to the HEC map renders the JUKF an excellent tool for our HEC-based map building, position estimation, motion planning and low-level tracking.

Genetic algorithms (GAs) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region in which the algorithm converges. Hybrid GAs are the combination of improvement procedures, which are good at finding local optima, and GAs. There are two basic strategies for using hybrid GAs. In the first, Lamarckian learning, the genetic representation is updated to match the solution found by the improvement procedure. In the second, Baldwinian learning, improvement procedures are used to change the fitness landscape, but the solution that is found is not encoded back into the genetic string. This paper examines the issue of using partial Lamarckianism (i.e., the updating of the genetic representation for only a percentage of the individuals), as compared to pure Lamarckian and pure Baldwinian learning in hybrid GAs. Multiple instances of five bounded nonlinear problems, the location-allocation problem, and the cell formation problem were used as test problems in an empirical investigation. Neither a pure Lamarckian nor a pure Baldwinian search strategy was found to consistently lead to quicker convergence of the GA to the best known solution for the series of test problems. Based on a minimax criterion (i.e., minimizing the worst case performance across all test problem instances), the 20% and 40% partial Lamarckianism search strategies yielded the best mixture of solution quality and computational efficiency.

Approaches in global motion planning (GMP) and geometric beacon collection (for self-localization) using traversability vectors have been developed and implemented in both computer simulation and actual experiments on mobile robots. Both approaches are based on the same simple, modular, and multifunctional traversability vector (t-vector). Through implementation it has been found that t-vectors reduce the computational requirements to detect path obstructions, Euclidean optimal via-points, and geometric beacons, as well as to identify which features are visible to sensors. Environments can be static or dynamic and polygons are permitted to overlap (i.e., intersect or be nested). While the t-vector model does require that polygons be convex, it is a much simpler matter to decompose concave polygons into convex polygon sets than it is to require that polygons not overlap, which is required for many other GMP models. T-vectors also reduce the data size and complexity of standard V-graphs and variations thereof. This paper presents the t-vector model so that the reader can apply it to mobile robot GMP and self-localization.

An approach to global motion planning for autonomous mobile robots has been developed on the basis of traversability vectors (t-vectors). Through the overall course of this research it was found that t-vectors provide a utility, efficiency and mathematical stability for collision detection and visibility that cannot be matched by commonly used algebraic approaches in static and dynamic environments. This paper will show that t-vectors also impact global motion planning by identifying redundancies in visibility graphs (V-graphs) and expediting their construction. The result of eliminating redundant path segments is a streamlined version of the V-graph called the essential visibility graph (EVG). This paper will also show that the EVG offers a significant reduction in data storage requirements and complexity.

A motion planning and self-referencing approach has been developed, simulated and applied to an actual robot. Although there are several novelties to these approaches, the fact that both are based on traversability vectors (t-vectors) is one aspect of this research that is unique. Through their application it has been found that t-vectors enhance the detection of path obstructions and geometric beacons and expedite the identification of features that are visible (or hearable) to sensors in both static and dynamic environments. T-vectors also reduce the data size and complexity of standard V-graphs and variations thereof. This paper provides the t-vector models step-by-step so that the reader will be able to apply them to mobile robot motion planning and self-referencing.>

When navigating through an environment, a mobile robot updates its position and orientation by searching for known objects called geometric beacons. This operation of comparing predicted sonar ranges with observed ones is referred to as self-referencing. There are two fundamental steps necessary for solving the self-referencing problem: how to represent the objects in the environment and how to search for landmarks based on this representation. This paper presents a method of mobile robot self-referencing where every object in the environment can be used as a landmark beacon. First, it shows how to interpret and predict sonar range readings through regional sampling and sensor window construction. Second, it justifies geometrically representing obstacles on the basis of surface information quality, compatibility with global motion planning, and the computer storage necessary to satisfy both criteria. Finally, it justifies the use of traversability vectors and configuration-space-time based on how they can be simultaneously used to plan motion globally and facilitate the search for geometric beacons.

Stationary visual cameras, or what is termed vision, can be used to provide the information necessary for the control of multiple free-ranging auto- matic guided vehicles in order to expand the range of applications for which they are a high-performance and cost-effective transport alternative. The camera placement problem of global vision refers to determining, for a given facility layout, a minimal number of cameras necessary to implement global vision-based control and their positions in the facility. In this paper, the camera placement prob- lem of global vision is formulated and analyzed for its computational complexity and is shown to be NP-hard, and a convex hull-based greedy algorithm is described for its approximate solution. I. INTRODUCTION Stationary visual cameras, or what is termed vision, can be used to provide the information necessary for the control of free-ranging automatic guided vehicle (AGV) systems. Global vision provides a means to robustly control AGV systems composed of large numbers of low-cost free- ranging vehicles that are able to operate in unrestricted indoor environments. The sensory information used for the control of the free-ranging AGV system is provided by global vision and low-cost range sensors on board each vehicle. A system with these capabilities will expand the range of applications for which they are a high-performance and cost-effective material transport alternative. The development of a means to achieve these capabilities was the principal motivation for proposing global vision-based free-ranging AGV control (3). Global vision refers to the use of cameras placed at fixed locations in a workspace to extend the local sensing available on board each vehicle in a free-ranging AGV system. Information from the cameras is used to (a) monitor the workspace to detect and track potential obstacles both in the immediate vicinity of each AGV and over its intended path; (b) track each AGV along its intended path to bound errors in the vehicle's dead-reckoning sensors; (c) monitor the load aboard each AGV to detect positioning errors; and, as a side benefit, (d) provide video images of the entire workspace so that a human operator can monitor the status of operations throughout the facility. Figure 1 shows the major components of global vision- based AGV control. The cameras for global vision are placed at fixed locations throughout a facility. Their unprocessed video signals are sent both to TV screens, where they could be used to monitor the status of on-going operations, and to a central processing site, where global vision-based vehicle control takes place. The status of the AGV system is available as input to high-level transport control functions (e.g., vehicle dispatching). Although only two cameras are shown in the figure, more cameras would typically be used in practice. Each vehicle has a communications link with the central pro- cessing site to receive control commands and to transmit the results of its on-board sensing operations. A. Limitations of Current AGV Systems

The issues involved in integrating multiple sensors into the operation of a system are presented in the context of the type of information these sensors can uniquely provide. A survey is provided of the variety of approaches to the problem of multisensor integration and fusion that have appeared in the literature in recent years ranging from general paradigms, frameworks, and methods for integrating and fusing multisensory information to existing multisensor systems used in different areas of application. General multisensor fusion methods, sensor selection strategies, and world models are examined, along with approaches to the integration and fusion of information from combinations of different types of sensors. Short descriptions of the role of multisensor integration and fusion in the operation of a number of existing mobile robots are provided, together with proposed high-level multisensory representations suitable for mobile robot navigation and control. Existing multisensor systems for industrial and other applications are considered.>