Keith C. Drake

Range Measurements by a Mobile Robot Using a Navigation Line

A continuous, straight-edged line is used for the visual navigation of an autonomous mobile robot in a factor environment. This line, which resides on the floor and contrasts with background, may also be used to determine range information. Two methods are developed for determining the range of an object in the sensors field of view. The effects of various error conditions in the system geometry on each ranging method are determined. Equations are derived which yield the percent error in calculating ranges given estimates of these error conditions. Numerical examples using typical sensor parameters are given.

Sensing Error for a Mobile Robot Using Line Navigation

The use of a contrasting line for the visual navigation of autonomous mobile robots in a factory environment is developed. Minimum and maximum linewidths are determined analytically by considering sensor geometry, field of view, and error conditions present in the system. The effects of these error conditions on the width of the line, as seen in the image plane, determines the optimal linewidth. Numerical examples using typical sensor parameters are given.

Experimental position and ranging results for a mobile robot

Experimental results are presented that support theory published in the literature concerning the use of a navigation line for the guidance of mobile robots. A method for the determination of a robots position is developed. A specialized edge operator, which aids in the segmentation of a navigation line from an image of a robots environment, is given. Use of this specialized edge operator in conjunction with the Hough transform is also presented. These methods are used to verify the analytical results given previously. Comparisons are made between experimental data and expected results as a function of various system parameters. Real-time implementation of these methods is considered.

Hierarchical integration of sensor data and contextual information for automatic target recognition

Real-time assessment of high-value targets is an ongoing challenge for the defense community. Many automatic target recognition (ATR) approaches exist, each with specific advantages and limitations. An ATR system is presented here that integrates machine learning, expert systems, and other advanced image understanding concepts. The ATR system employs a hierarchical strategy relying primarily on abductive polynomial networks at each level of recognition. Advanced feature extraction algorithms are used at each level for pixel characterization and target description. Polynomial networks process feature data and situational information, providing input for subsequent levels of processing. An expert system coordinates individual recognition modules.Heuristic processing of object likelihood estimates is also discussed. Here, separate estimators determine the likelihood that an object belongs to a particular class. Heuristic knowledge to resolve ambiguities that occur when more than one class appears likely is discussed. In addition, a comparison of model-based recognition with the primary polynomial network approach is presented. Model-based recognition is a goal-driven approach that compares a representation of the unknown target to a reference library of known targets. Each approach has advantages and limitations that should be considered for a specific implementation.This ATR approach can potentially overcome limitations of current systems such as catastrophic degradation during unanticipated operating conditions, while meeting strict processing requirements. These benefits result from implementation of robust feature extraction algorithms that do not take explicit advantage of peculiar characteristics of the sensor imagery; and the compact, real-time processing capability provided by abductive polynomial networks.

Hierarchical polynomial network approach to automated target recognition

A hierarchical recognition methodology using abductive networks at several levels of object recognition is presented. Abductive networks--an innovative numeric modeling technology using networks of polynomial nodes--results from nearly three decades of application research and development in areas including statistical modeling, uncertainty management, genetic algorithms, and traditional neural networks. The systems uses pixel-registered multisensor target imagery provided by the Tri-Service Laser Radar sensor. Several levels of recognition are performed using detection, classification, and identification, each providing more detailed object information. Advanced feature extraction algorithms are applied at each recognition level for target characterization. Abductive polynomial networks process feature information and situational data at each recognition level, providing input for the next level of processing. An expert system coordinates the activities of individual recognition modules and enables employment of heuristic knowledge to overcome the limitations provided by a purely numeric processing approach. The approach can potentially overcome limitations of current systems such as catastrophic degradation during unanticipated operating conditions while meeting strict processing requirements. These benefits result from implementation of robust feature extraction algorithms that do not take explicit advantage of peculiar characteristics of the sensor imagery, and the compact, real-time processing capability provided by abductive polynomial networks.

Pixel-level object segmentation using moment invariants as characterization features

This paper focuses on the application of moment invariants as pixel-level characterization features. An innovative machine learning paradigm used to automatically learn information fusion models is briefly presented.

Decision criterion processing methodology for heuristic assessment of classification models

Detection, classification, and identification of high-value targets are ongoing challenges for the defense research community. Many automatic target recognition approaches exist, each with specific advantages and limitations. This approach first segments potential targets at the pixel level, followed by several hierarchical levels of object classification and identification. This paper discusses a specific aspect of this paradigm--the heuristic assessment of object classification likelihood estimates.

Sensor roll angle error for a mobile robot using a navigation line

An investigation of the effects of various error components in a mobile robot system, which uses a navigation line, was previously reported by the author in 1985. An analysis of the effects of these error components on the navigation line-width determination and obstacle-range determination is presented that provided equations that are used to predict the error resulting from changes in various system parameters. In particular, the change in the rotation angle of the robot sensor about its optical axis, called the roll angle, is examined. Numerical examples showing the characteristics of the derived error equations are also given. >

Pixel-level object segmentation from multispectral sensor imagery

Successful object classification is highly dependent upon initial segmentation of an object from its background. For complex, real-world imaging applications, this task is extremely challenging and critical to the success of the recognition system. Traditional object segmentation techniques often rely heavily upon noise removal during preprocessing and subsequently employ image-level segmentation strategies. Because effective noise-removal strategies are often difficult to develop for real-world imagery, alternate methods are required for object segmentation. An alternate approach is to determine target/nontarget status of image regions at the pixel level. In this manner, noise removal and object segmentation are performed in a single process. The approach takes advantage of the large amount of information contained in present-day, multispectral imagery. The key issues associated with this approach are a robust pixel information representation and an information fusion algorithm to process pixel-level information.