Dennis J. Wenzel

A novel approach to robot control using fuzzy logic

The authors have developed a robot controller that exploits the principles of fuzzy logic to circumvent the mathematically complex inverse kinematic equations that are at the heart of operations of conventional robot controllers. They describe the derivation of the fuzzy control rules for a planar robot with an arbitrary number of serial degrees-of-freedom (DOF). Computer simulation results are presented to verify the proposed concept. In addition, experimental results of implementing the developed fuzzy logic controller on a four DOF planar laboratory robot are discussed. Bench mark tests comparing the execution speed of the proposed fuzzy logic controller with the traditional controller revealed that the approach described was 33% times faster than traditional methods, which require solution of the inverse kinematic equations.<<ETX>>

An optimal material removal strategy for automated repair of aircraft canopies

A description is given of the Robotic Canopy Polishing System (RCPS). The RCPS workcell contains three robotic arms that are used to assist an expert canopy polisher in inspecting and reworking plastic aircraft canopy transparencies. Particular attention is given to the methodology by which the RCPS determines where best to remove plastic from the canopy, given the raw data indicating flaw positions and severity. This process is driven by the following requirements: to completely remove the flaws; to minimize the number of grinding patterns laid on the canopies; and to minimize the distortion by overlapping grinding patterns. The features of the template-mismatch technique used are described.<<ETX>>

Adaptation of group algebras to signal and image processing

Abstract Filtering techniques in digital signal and image processing typically use linear and/or cyclic convolutions, both of which are based on polynomial arithmetic. When the input data and the filter weights are treated as coefficients of polynomials, linear convolution is computed directly as a product of these polynomials. Cyclic convolution is used as an efficient means of computing the linear convolution. In this case, the input is organized into sections, each section treated as the coefficients of a polynomial. The output data are generated by selecting the last coefficients of the products of the section polynomials with the filter polynomial. We develop a more general methodology for filtering signals and images on the basis of the ideas of: (1) group algebras, a generalization of polynomials and polynomial arithmetic, and (2) value functions, a generalization of the coefficient selection used in generating the output data. Applications of these ideas to signal and image processing are given. Finally, the concepts are incorporated into the framework of image algebras.

High-speed extraction of line segment features

A high-speed algorithm for extracting line segment features in an image is described. The extracted line segments are used as the geometric features for identifying and locating objects of interest, specifically in a Model Based Vision system. The algorithm is divided into two parts with each part being performed using separate computer hardware. The first part of the algorithm involves locating pixels in an image which correspond to edges. This part of the algorithm was implemented on the Datacube MaxVideo-20 pipelined image processing hardware executing in real time. The second part of the algorithm involves the extraction of the edge pixels in a connected manner so that line segments can be identified. This part of the algorithm was implemented in software on a Sun Sparc 2 workstation using a run-length encoded image and a chain-code mapped image generated by the Datacube MaxVideo-20 hardware.

Multidimensional pattern classification of bottles using diffuse and specular illumination

Abstract A robust and fast method for segmenting and recognizing bottles based on features extracted only from their circular tops is presented. The approach exploits a dual (stereo) camera system that views trays of bottles illuminated by one of two spatially separated sequenced light sources. The first of these light sources produces specularly illuminated bottle caps while the other diffusely illuminates them. The images obtained using light sources that produce specular reflections are employed primarily for segmentation purposes, although two features based on mean specular intensity and bottle height are extracted from these images. The images obtained while the diffuse light sources are active are used to extract three other features which are the mean diffuse intensity, intensity variance, and intensity distribution of the bottle caps. It is shown that this dual lighting approach increases the overall reliability of the integrated system which must be capable of accurately segmenting and recognizing many bottle types. Results of test runs involving sets of hundreds of training samples and unknowns are presented and demonstrate that recognition rates in excess of 99% may be expected.

Telerobot control using enhanced stereo viewing

A telerobot control system using stereoscopic viewing has been developed. The objective of the system is to implement a world-model mapping capability using live stereo video to provide an operator with three-dimensional image information of an unstructured environment and to use stereo computer graphics renderings of wire-frame models of sought-after features in the environment in order to relate robotic task information. The operator visually correlates or matches the stereo video image of the environment with the graphic image to register the world model space to the manipulators environment. This allows operator control of the manipulator through teleoperation in unstructured environments with a change-over to autonomous operation when the operation can be restricted and a task becomes repetitive. Details of the robot control, stereo imaging, and system control components are provided.

Advanced visualization technology project (AVTP): an architecture for visualization using remote parallel/distributed computing

The principles of scalable computing have been used in an investigation of the application of high speed data networks and remote computer resources in providing visualization tools for research and development activities. The architecture of a distributed visualization system that can utilize either shared memory or message passing paradigms is described. The three components of the system can be physically separated if network communication is provided. A flexible data cache server is used to accommodate newly computed data or data from an earlier experiment or computation. An image specification toolset, implemented for parallel/distributed architectures using PVM, includes methods of calculating common visualization forms such as vector fields, surfaces or streamlines from cache data. An image generation library, implemented for workstations and high performance PCs, receives the data objects and provides investigators with flexibility in image display. The system has been operated with several combinations of distributed and parallel processor machines connected by networks of different bandwidths and capacities. Observations on the performance and flexibility of different system architectures are given.© (1995) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Group Algebras in Signal and Image Processing

Publisher Summary The linear and cyclic convolutions are computed as polynomial products. Group algebras are generalizations of polynomials and polynomial arithmetic based on algebraic groups. The multiplication in group algebra is called “group convolution.” The selection of coefficients from the cyclic convolution to generate output for the linear convolution can be extended to other functions called “value functions,” to allow for a wider variety of outputs. Group convolution of a signal requires selecting a single base group to work with. For images, group convolution is naturally based on the direct product of groups. Matrix representation of group algebra elements is discussed. The chapter discusses direct product group algebras. The ideas extend to convolution of data sets whose dimension is larger than two. Semidirect product group algebras that are used mainly in studying group algebras based on dihedral groups are discussed. The chapter also discusses inverting group algebra elements. Two methods for inverting elements are explained. In applications where nonzero and noninvertible group algebra elements may be a problem, a way to eliminate these elements via ideals and quotient rings is presented. Matrices whose elements are from group algebra are discussed. The chapter focuses on those matrices that have the convolution property—that is, those matrices for which the transform of a convolution is the product of the individual transforms. The chapter presents examples of the application of group algebras to edge detection in signals and images. The examples illustrate the use of direct product cyclic groups (the standard convolution case), direct product dihedral groups, and direct product quaternions. The chapter describes the process to implement group algebras in an object-oriented programming language.

Recognition of containers using a multidimensional pattern classifier

A method for recognizing closed containers based on features extracted from their circular tops is presented. The approach developed consists of obtaining images from two spatially separated cameras that utilize both diffuse and specular light sources. The images thus obtained are used to segment target objects from the background and to extract representative features. The features utilized consist of container height as computed using stereopsis as well as the mean, variance, and second central moments of the intensities of the segmented caps. The recognition procedure is based on a minimum distance Mahalanobis classifier which takes feature covariance into account. The discussion that follows details the algorithmic approach for the entire system including image acquisition, object segmentation, feature extraction, and pattern classification. Result of test runs involving sets of several hundred training samples and untrained samples are presented.