James Stewart Rankin

Torque control for a form tool drilling operation

This paper presents the dynamic modeling and real-time torque control for a form tool drilling process. The form tool produces a desired shape in a workpiece through a drilling process. In this study, the form tool drilling process resembles the combination of drilling, reaming, counter-boring, and chamfering operations. The machining process is modeled as a linear system with variable gain due to the form tool geometry. The process is also subject to unknown disturbances such as unpredictable chip jamming and tool-workpiece friction. Spindle motor power and speed measurements were used to estimate drilling torque for cost effective practical implementation. An input-output pole placement controller with previewed gain scheduling was designed and implemented. Through experimental studies, this controller was shown to effectively regulate tool torque and hence avoid tool breakage, by manipulating the feed during drilling, and reduce cycle time compared to current practice.

Automated CAD-guided automobile part dimensional inspection

Structured light is one of the well-known methods in part dimensional inspection that have been successfully employed in various applications in the past decades. In this method, the positioning of the camera is very critical, which affects the accuracy and efficiency of the whole inspection system. Here we develop a CAD-guided camera positioning system to aid the 3-D part inspection. The geometric information in the CAD model of the inspected part and the camera model are used to plan camera configurations that satisfy certain task constraints. The overall system we propose can be applied to the 3-D inspection of parts with free-form surfaces, such as automobile door panels. Experiments on different parts show satisfying results.

Fuzzy-logic-based sensor fusion of images

The fusion of visual and infrared sensor images of potential driving hazards in static infrared and visual scenes is computed using the Fuzzy Logic Approach (FLA). The FLA is presented as a new method for combining images from different sensors for achieving an image that displays more information than either image separately. Fuzzy logic is a modeling approach that encodes expert knowledge directly and easily using rules. With the help of membership functions designed for the data set under study, the FLA can model and interpolate to enhance the contrast of the imagery. The Mamdani model is used to combine the images. The fused sensor images are compared to metrics to measure the increased perception of a driving hazard in the sensor-fused image. The metrics are correlated to experimental ranking of the image quality. A data set containing IR and visual images of driving hazards under different types of atmospheric contrast conditions is fused using the Fuzzy Logic Approach (FLA). A holographic matched-filter method (HMFM) is used to scan some of the more difficult images for automated detection. The image rankings are obtained by presenting imagery in the TARDEC Visual Perception Lab (VPL) to subjects. Probability of detection of a driving hazard is computed using data obtained in observer tests. The matched-filter is implemented for driving hazard recognition with a spatial filter designed to emulate holographic methods. One of the possible automatic target recognition devices implements digital/optical cross-correlator that would process sensor-fused images of targets. Such a device may be useful for enhanced automotive vision or military signature recognition of camouflaged vehicles. A textured clutter metric is compared to experimental rankings.

Noise and contrast comparison of visual and infrared images of hazards as seen inside an automobile

The purpose of this experiment was to quantitatively measure driver performance for detecting potential road hazards in visual and infrared (IR) imagery of road scenes containing varying combinations of contrast and noise. This pilot test is a first step toward comparing various IR and visual sensors and displays for the purpose of an enhanced vision system to go inside the driver compartment. Visible and IR road imagery obtained was displayed on a large screen and on a PC monitor and subject response times were recorded. Based on the response time, detection probabilities were computed and compared to the known time of occurrence of a driving hazard. The goal was to see what combinations of sensor, contrast and noise enable subjects to have a higher detection probability of potential driving hazards.

A Recursive, Line-Intersection Method for Finding the Area of a Mesh Projected Onto a Plane

In this paper, we describe a new approach for computing the area of a mesh projected onto a plane. This approach utilizes the graphics hardware’s line/object intersection capability and a recursive subdivision strategy to achieve performance and precision control. This approach starts from digitizing the projection plane into a grid of rectangular elements. For each element the graphics engine is utilized to check whether projection lines passing through the nodes of the element intersect the object in the model space. If all lines intersect the object, the element is considered “inside” and its area will be accounted towards the final projection area. If none of the lines has an intersection, the element is considered “outside” and discarded. For those elements that lay along the boundary of the projected area (which means some of their lines intersect the model while others don’t) we subdivide them until they are sufficiently small and the given area tolerance is met. Heuristics are derived for deciding the initial grid resolution and the level of subdivisions needed to meet/exceed a given area tolerance. Implementation results are demonstrated and compared with a classic polygon-clipping approach.Copyright