Nowadays, robust and light-weight parts used in the automobile and aeronautics industry are made of carbon fibres. To increase the mechanical toughness of the parts the carbon fibres are stitched in the preforming process using a sewing robot. However, current systems miss high flexibility and rely on manual programming of each part. The main target of this work is to develop an automatic system that autonomously sets the structure strengthening seams. Therefore, a rapid and flexible following of the carbon textile edges is required. Due to the black and reflective carbon fibres a laser-stripe sensor is necessary and the processing of the range data is a challenging task. The paper proposes a real time approach where different edge detection methodologies are combined in a voting scheme to increase the edge tracking robustness. The experimental results demonstrate the feasibility of a fully automated, sensor-guided robotic sewing process. The seam can be located to within 0.65mm at a detection rate of 99.3% for individual scans.

Fast detection of objects in a home or office environment is relevant for robotic service and assistance applications. In this work we present the automatic localization of a wide variety of differently shaped objects scanned with a laser range sensor from one view in a cluttered setting. The daily-life objects are modeled using approximated superquadrics, which can be obtained from showing the object or another modeling process. Detection is based on a hierarchical RANSAC search to obtain fast detection results and the voting of sorted quality-of-fit criteria. The probabilistic search starts from low resolution and refines hypotheses at increasingly higher resolution levels. Criteria for object shape and the relationship of object parts together with a ranking procedure and a ranked voting process result in a combined ranking of hypothesis using a minimum number of parameters. Experiments from cluttered table top scenes demonstrate the effectiveness and robustness of the approach, feasible for real world object localization and robot grasp planning.

Vision systems are more and more used in the fields of industrial automation and quality control. This paper describes a 3D vision system that guides a robot arm to reliably insert an endoscope into a bore hole for a subsequent bore surface inspection. The already industrial deployed system, developed in the project FibreScope [3], performs a quality control of bores with a diameter ranging from 4 to 50mm and a depth of up to 100mm. This requires a bore detection accuracy of the vision system of "}0.3mm and less than 0.5 in 3D space to modify the robot path for safely inserting the endoscope. The challenge in the design of the vision system was that it must work in process real time achieving the required accuracy. This paper introduces the prototype of the robotic system and the 3D vision system. A performance evaluation of the 3D vision system and its bore localization and detection proofs the need of a vision system to compensate positioning uncertainties for a flexible robotic inspection system.

We present enhancements of existing calibration methods that allow the automated calibration of a laser stripe profiler in an industrial environment. The first and most important enhancement is a new design of the calibration object. Basically it is a “wire-frame model” of a cube, where the edges are simply standard aluminium profiles. This allows establishing point correspondences in a simple and robust way without the need of intensity information. Furthermore this hollow kind of a calibration object allows simultaneous multi-view calibration since enough points of six different planes can be seen from many directions. Given a rough calibration (based on rough measurements with ± 50% accuracy) the scanned points can be automatically allocated to the respective planes of the calibration object. Secondly we propose a new way of solving the linear model of a laser stripe profiler. By firstly solving the camera-independent geometry these values can be averaged over the multiple views, which further reduces the local offsets of point clouds and therefore enhances the output of subsequent operations like mesh generation on the combined calibrated point cloud. The paper describes the design of the calibration object and the calibration method in detail and presents experimental results on simulated and real range data sets. The industrial applicability has been demonstrated in a sensor cell with four cameras under real-world conditions including moderate oscillations of the calibration object.

A persistent difficulty of indoor robot navigation is to reliably locate tables for navigation or service tasks. The predominately used laser range finders have difficulties to detect thin metallic table legs and protruding table surfaces. This papers presents a simple yet robust approach to locate table surfaces using stereo vision in home and office environments. Disparity image data is used to first locate the ground plane with an approach building on the results in [3]. Exploiting the expectation of the floor plane, parallel surfaces are detected. Robustness is achieved by using a combination of local grouping processes and probabilistic estimation to cope with the noisy disparity data and to cope with the typically large number of spurious data points. Tables are detected and located independently of clutter on the table or the type of legs. Experiments in a home environment show detection accuracy of less than two centimeters in estimating the table or chair surface height.

Bores and internal threads play a critical role as integral parts of connections, bearings and engines, hydraulic and pneumatic systems. Defective bore surfaces lead to increased friction and abrasion, leakage, and reduced stability. Therefore, industry demands ever higher quality of components' bore surfaces. Today industrial automation is limited to high part volumes. Current systems miss a high flexibility or are mainly driven by human interaction and control. Therefore the main target of this work is to develop an automatic system for rapid and flexible 100% surface inspection of bores with diameters from 4 to 50 mm. Further a method for the detection and localization of bores on arbitrary metallic objects has to be developed as well as the image processing for the inspection with a vision system has to be done. The main focus is on easy operating of the system achieved with rapid programming for the user. The paper describes the system, in overview and detail, and shows the promising results of the experiments and a public long-term demonstration (4 days) during an industrial fair.

This paper presents an approach to detect classes of features that are relevant for automating spray painting. Using knowledge about the painting process a set of elementary geometries is defined, where each elementary geometry is related to a specific painting strategy. Hence all parts and part families containing these elementary geometries can be detected. After detection the paint strokes are automatically generated for robot programming. Specifically we show how free-form surfaces, cavities and rib sections are detected in the range image of the parts. Results of detecting these features on a large variety of parts are presented.