Abstract
The accuracy of a capacitive proximity sensor is affected by various factors, including the geometry and composition of the nearby object. The quantitative regression models that are used to seek out the relationship between the measured capacitances and distances to objects are highly dependent on the geometrical properties of the objects. Consequently, the application of capacitive proximity sensors has been mainly limited to detection of objects rather than estimation of distances to them. This paper presents a capacitive proximity sensing system for the detection of metallic objects with improved accuracy based on target profile estimation. The presented approach alleviates large errors in distance estimation by implementing a classifier to recognize the surface profiles before using a suitable regression model to estimate the distance. The sensing system features an electrode matrix that is configured to sweep a series of inner-connection patterns and produce features for profile classification. The performance of the sensing modalities is experimentally assessed with an industrial robot. Two-term exponential regression models provide a high degree of fittings for an object whose shape is known. Recognizing the shape of the object improved the regression models and reduced the close-distance measurement error by a factor of five compared to methods that did not take the geometry into account. The breakthroughs made through this work will make capacitive sensing a viable low-cost alternative to existing technologies for proximity detection in robotics and other fields.
Highlights
The demand for industrial robots has accelerated considerably due to the ongoing trend toward automation and continued innovative technical improvements in industrial robots within the past decade
The cylinder was brought in at different in-plane angles. With all these variations in positioning and relative angles, the objective was to improve the accuracy of distance estimation from the electrode array to the closest point on the object by taking into account the object geometry regardless of approaching angles or lateral position of the object relative to the electrode array
Shunt detecting mode is used in combination with the 4 × 4 electrode matrix to provide more informative and flexible measurements
Summary
The demand for industrial robots has accelerated considerably due to the ongoing trend toward automation and continued innovative technical improvements in industrial robots within the past decade. Robots and humans present complementary features for the development of manufacturing processes; the close cooperation of human and machine is highly demanded [1]. A great deal of attention has been paid to the human workers’ safety as collisions between the worker and the robotic manipulators can be extremely dangerous. The data on industrial robot-related fatalities indicate that safety remains a major concern, especially because the human operators are by necessity physically close to mechanical arms or vehicles [2]. One highlighted approach that modifies the robot’s trajectory based on safety zones or separation distances has shown its superiority in practical applications. In this technique, nonintrusive sensors for distance measurement and localization are critical
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