Autonomous vehicles require that all on-board processes be efficient in time, complexity and data storage. Infact, an ideal system employs multi-funcitonal models where ever possible. The research documented hereproposes that the Region-Feature Neural Network (RFNN) and the Hyper-Ellipsoid Clustering (HEC)Kohonen neural network (or HECNN) are viable pattern analysis and control engines that contribute to thesolution of a variety of problems. The theoretical development of the RFNN and HECNN, along with severalproof-of-concept applications are presented in detail. The RFNN is a feed-forward, back-propagation modelthat is more general than standard textbook models because it also accomodates receptive fields and weightsharing. The RFNN uses a modified version of adaptive learning rates, called "shocking" to reduce training timeand maintain stability. Small-scale benchmark problems like the XOR and XOP problems are used todemonstrate the utility of the "shocking" model. Due to its modularity, the RFNN allows the user to constructflexible, multi-layered, feed-forward architectures as well as add to and prune from an architecture even aftertraining has begun. The RFNN also permits the user to include previously learned features, called "analogies" tofurther expedite the training process on new problems or whenever new classes are added. The HECNN is aself-organizing neural network that incorporates hyperellipsoid clustering by use of the Mahalanobis distance tolearn elongated shapes and obtain a stochastic measurement of data-node association. The number of nodes canalso be regulated in a self-organizing manner by measuring how well each node models the statistical propertiesof its associated data. This measurement, called "compactness", determines where and whether to add neuralunits or prune them completely. We make several enhancements to the Kolmogorov-Smirnov compactness testto control the triggering of mitosis and/or pruning. Because fewer nodes are needed for an HECNN than for aKohonen that uses only Euclidean distance, the data size is smaller for the HEC Kohonen, even forhigh-dimensional problems. The large-scale pattern analysis problems presented here for the RFNN includesonar pattern recognition and outdoor landmark recognition. For the HECNN, we focus on sonar patternrecognition and (topographical) map building. Both the RFNN and the HECNN can be generalized to solve orcontribute to the solution of other pattern recognition problems. Both are also multifunctional in that theyaccommodate compact geometric motion planning (MP), self-referencing (SR) and tracking algorithms.Additionally, we propose the "traversability vector" (t-vector) as an efficient bridge between the HECNN andboth motion planning and self-referencing for mobile robots. As with the RFNN and HECNN, the t-vector is amodular and multi-functional tool that minimizes the computation requirements and data size as it detects pathobstructions, Euclidean optimal via points, and geometric beacons, as well as identify which geometric featuresare visible to sensors in environments that can be static or dynamic. Tracking is made possible with Julier andUhlmann's unscented filter. The unscented filter particularly compliments the HECNN in that it performslow-level (non-linear) tracking more efficiently and more accurately than its predecessor, the extended Kalmanfilter (EKF). By estimating and propagating error covariances through system transformations, the unscentedfilter eliminates the need to derive Jacobian matrices. The inclusion of stochastic information inherent to the HECmap rendered the JUKF an excellent tool for our HEC-based map building, global self-localization, motionplanning and low-level tracking.
