John C. Sutton

Autonomous mobile robot global self‐localization using Kohonen and region‐feature neural networks

This article presents and compares two neural network-based approaches to global selflocalization (GSL) for autonomous mobile robots using: (1) a Kohonen neural network; and (2) a region-feature neural network (RFNN). Both approaches categorize discrete regions of space (topographical nodes) in a manner similar to optical character recognition (OCR). That is, the mapped sonar data assumes the form of a character unique to that region. Hence, it is believed that an autonomous vehicle can determine which room it is in from sensory data gathered from exploration. With a robust exploration routine, the GSL solution can be time-, translation-, and rotation-invariant. The GSL solution can also become independent of the mobile robot used to collect the sensor data. This suggests that a single robot can transfer its knowledge of various learned regions to other mobile robots. The classification rate of both approaches are comparable and, thus, worthy of presentation. The observed pros and cons of both approaches are also discussed.  1997 John Wiley & Sons, Inc.

Self-organizing geometric certainty maps: a compact and multifunctional approach to map building, place recognition and motion planning

In this paper we show how a self-organizing Kohonen neural network can use hyperellipsoid clustering (HEC) to build maps from actual sonar data. Since the HEC algorithm uses the Mahalanobis distance, the elongated shapes (typical of sonar data) can be learned. The Mahalanobis distance metric also gives a stochastic measurement of a data points association with a node. Hence, the HEC Kohonen can be used to build topographical maps and to recognize its own topographical cites for self-localization. The number of nodes can also be regulated in a self-organizing manner by using the Kolmogorov-Smirnov (KS) test for cluster compactness. The KS test determines whether a node should be divided (mitosis) or pruned completely. By incorporating principal component analysis, the HEC Kohonen can handle problems with several dimensions (3D, time-series, etc.). The HEC Kohonen is also multifunctional in that it accommodates compact geometric motion planning and self-referencing algorithms. It can also be used to solve a host of other pattern recognition problems.

Using a hyper-ellipsoid clustering Kohonen for autonomous mobile robot map building, place recognition and motion planning

We show how a self-organizing Kohonen neural network using hyperellipsoid clustering (HEC) can build maps from actual sonar data. With the HEC algorithm we can use the Mahalanobis distance to learn elongated shapes (typical of sonar data) and obtain a stochastic measurement of data-node association. Hence, the HEC Kohonen can be used to build topographical maps and to recognize its own topographical cues for self-localization. The number of nodes can also be regulated in a self-organizing manner by measuring how well a node models the statistical properties of its associated data. This measurement determines whether a node should be divided (mitosis) or pruned completely. Because fewer nodes are needed for an HEC Kohonen than for a Kohonen that uses only Euclidean distance, the data size is smaller for the HEC Kohonen. Relative to grid-based approaches, the savings in data size is even more profound. By incorporating principal component analysis (PCA), the HEC Kohonen can handle problems with several dimensions (3D, time-series, etc.). The HEC Kohonen is also multifunctional in that it accommodates compact geometric motion planning and self-referencing algorithms. It can also be generalized to solve other pattern recognition problems.

Fusing a hyper-ellipsoid clustering Kohonen network with the Julier-Uhlmann-Kalman filter for autonomous mobile robot map building and tracking

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.

A multiple perspective printed circuit board design guide: expert system prototype

An expert system prototype was developed to provide a merit rating for design options selected by a printed circuit board (PCB) designer. Experts in design, manufacturing, test and procurement from three companies were used to develop the knowledge base using a preselected PCB as a prototype. The system is designed to act as a guide toward accepted practice and to encourage consideration of manufacturing engineering, component selection, and test perspectives as well as product design orientation. The process involved team consensus building and problem solving among cross disciplinary functions within each company while capturing quantitative and qualitative design information in a structured transferable format. An internal and external knowledge base were created to promote flexibility in use for different products and varied company standards. The internal knowledge base contains system structure, chaining strategy, knowledge confirmation and calculation methodology. The external knowledge base contains issue weights, options, values, and descriptions. The user can modify and update the external knowledge base without depending on a system professional. Any or all of the overlapping frame-based issues may be selected when evaluating the design. Special attention is given to issues relevant to several branch and design options. AI TOPIC: Configuration and Design DOMAIN AREA: Printed Circuit Board Design LANGUAGE TOOL: Personal Consultant Plus STATUS: Working Prototype, Proposal Pending for Developed System EFFORT: Two Person Years IMPACT: This printed circuit board design guide promotes problem solving among the cross disciplinary functions of design, test, procurement, and manufacturing.