The paper proposes a high-accuracy, large-scale robotic visual inspecting system which consists of an industrial robot and an optical scanning sensor fixed on the robot hand. Traditionally, calibration of a robotic visual inspecting system is separated into three parts: Hand-eye calibration, robot calibration and robot exterior calibration. Compared with the traditional calibration method, this paper presents a new self-calibration method to calibrate and compensate for the robotic visual inspecting system's kinematic errors. The proposed calibration approach has two unique features: First, it can be calibrated without external measurement devices and human intervention. Second, it simultaneously calibrates the kinematic parameters of the whole inspecting system in one mathematic model to avoid error propagation. These features not only realize the automatic calibration of the inspecting system but also avoid error propagation. Experiments are conducted on a 6 DOF serial robot to validate the good performance of the proposed method.
Reverse engineering, the process of obtaining a geometric CAD model from measurements obtained by scanning an existing physical model, is widely used in numerous applications, such as manufacturing, industrial design and jewellery design and reproduction. For creating editable CAD models meant for manufacturing we identify that it is more appropriate to use feature-based constraint-based representations, since they capture plausible design intent. We propose this type of model representation for reverse engineering 3D point clouds of jewellery objects. In this paper we propose an approach for reverse engineering of jewellery combining skeleton construction, feature and constraint information exploitation to obtain a more robust and accurate CAD model. First we automatically construct the skeleton of the point cloud. Constraints are automatically detected based on the skeleton and then an iterative interactive process is carried out, during which features are fitted to the point cloud according to constraints. A voxel inspired technique is also employed to describe repeated patterns common to various types of traditional jewellery.
Robot calibration plays an increasingly important role in manufacturing. For robot calibration on the manufacturing floor, it is desirable that the calibration technique be easy and convenient to implement. This paper presents a new self-calibration method to calibrate and compensate for robot system kinematic errors. Compared with the traditional calibration methods, this calibration method has several unique features. First, it is not necessary to apply an external measurement system to measure the robot end-effector position for the purpose of kinematic identification since the robot measurement system has a sensor as its integral part. Second, this self-calibration is based on distance measurement rather than absolute position measurement for kinematic identification; therefore the calibration of the transformation from the world coordinate system to the robot base coordinate system, known as base calibration, is not necessary. These features not only greatly facilitate the robot system calibration but also shorten the error propagation chain, therefore, increase the accuracy of parameter estimation. An integrated calibration system is designed to validate the effectiveness of this calibration method. Experimental results show that after calibration there is a significant improvement of robot accuracy over a typical robot workspace.
Image registration is the process of aligning the corresponding features of images in the same coordinate system. Multimodal registration has been widely used in medical imaging and geographic imaging. However, it has not been broadly applied in the inspection imaging of mechanical parts. Multimodal registration can improve inspection accuracy and quality by combining complementary inspection data from different inspection methods, or “modalities”. The research focus of this work is to develop a computational algorithm to register a CMM point cloud with a CT image in the 2-D (planar) domain. Dealing with outliers is the major concern for achieving required registration accuracy. Targeting solving this problem, a new registration metric is proposed in this work, which makes application of the traditional ICP (Iterative Closest Point) algorithm robust, by optimizing the search for closest points.
Data fusion is a key step in multimodal inspection of mechanical parts. It combines complementary data from different inspection methods, or “modalities”, to improve inspection accuracy. Data fusion has broad applications in the disciplines of the military, medicine, and geography. It has attracted increasing attention from industry in recent years. However, its industrial application has been limited mainly to 2D (2-Dimensional) domains. The research focus of this work is to develop a data fusion system to automatically fuse and extract the most accurate information from each modality in the domain of 3D. The main process includes automatically filtering and interpolating CMM data, registering CMM data with CT data, detecting the CT slices to generate interpolated CMM constraints for iterative CT reconstruction, and replacing exterior CT data with interpolated CMM data. This technology enables us to achieve high accuracy for external structures and provide constraints for CT reconstruction to improve the accuracy for internal boundary data as well.
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