Thomas Schlegl

Smart Workbench: A multimodal and bidirectional assistance system for industrial application

Manual tasks in industrial production are often monotonous, leading to a decrease in concentration and motivation of the worker and thus to deficiencies in the products. With quality as well as performance requirements getting more and more stringent, workers need additional support by their work environment. We developed a novel approach providing worker assistance and inline quality assurance for manual workplaces. The prototypical system Smart Workbench (SWoB) uses a multimodal sensor interface consisting of a 3D depth sensor in combination with a 2D camera to control quality aspects of the product and track the work progress. The bidirectional flow of information is handled via an image processing driven gestural interface and the displaying of advice directly on the work surface. In this paper the developed system is introduced and the current state of evaluating its industrial usage with a manual quality control and packaging task reported.

Modeling, identification and control of an antagonistically actuated joint for telerobotic systems

Within this paper a modeling, identification and control technique for an antagonistically actuated joint consisting of two pneumatically actuated muscles is presented. The antagonistically actuated joint acts as a test bench for control architectures which are going to be used to control an exoskeleton within a telerobotic system. A static and dynamic model of the muscle and the joint is derived and the parameters of the models are identified using a least-squares algorithm. The control architecture, consisting of a inner pressure and an outer position controller is presented. The pressure controller is evaluated using switching valves compared against proportional valves.

Ultrasound Based Object Detection for Human-Robot Collaboration

For safe human-robot collaboration within a defined working area, a technologically diverse and redundant sensor system is developed, which comprises ultrasound sensors and two monocular cameras. The ultrasound sensor system, as well as the developed algorithms for the sensor system are proposed, which allow a distinction between objects. Detected objects within the working area are classified in static and dynamic objects. The sensor system is able to distinguish between these object types. Due to the safe detection of dynamic objects the robot system is enabled to react with an adaption of its trajectories to avoid undesired collisions. To ensure, that the manipulator only reacts on dynamic objects, distances caused by static objects are eliminated.

Modeling and nonlinear control of antagonistically actuating pneumatic artificial muscles

This paper discusses modeling and nonlinear control of a joint antagonistically actuated by two pneumatic, artificial muscles. A single model of the whole system is obtained by a combined physical and phenomenological modeling approach. The combined model for the joint, the muscles and the proportional valves results in a nonlinear, affine-in-control system description. The model is used to derive control laws for an input/output linearization approach to linearize the plant. Modeling and parametrization errors are covered via an outer control loop consisting of a state-feedback which is extended by an additional feedback of error integral. Extensive experimental results show the quality of the model and the performance of the respective control laws.

Motion Analysis of Human-Human and Human-Robot Cooperation During Industrial Assembly Tasks

This article discusses the relevance of the motion behavior and adaptation of a collaborative robot for human-robot cooperation. Two experiments on cooperative assembly are shown. First, a human-human experiment with defined test conditions evaluates the aspects of distance, nearest body part, and predictability as significant. Second, a human-robot experiment shows that fixed trajectories and conservative dynamic parameters lead to a quick gain of confidence of the participants. Besides, the data shows that a realistic use case with complex tasks is key to evaluate the impact of motion parameters.

Impact of Spontaneous Human Inputs during Gesture based Interaction on a Real-World Manufacturing Scenario

Seamless human-robot collaboration depends on high non-verbal behaviour recognition rates. To realize that in real-world manufacturing scenarios with an ecological valid setup, a lot of effort has to be invested. In this paper, we evaluate the impact of spontaneous inputs on the robustness of human-robot collaboration during gesture-based interaction. A high share of these spontaneous inputs lead to a reduced capability to predict behaviour and subsequently to a loss of robustness. We observe body and hand behaviour during interactive manufacturing of a collaborative task within two experiments. First, we analyse the occurrence frequency, reason and manner of human inputs in specific situations during a human-human experiment. We show the high impact of spontaneous inputs, especially in situations that differ from the typical working procedure. Second, we concentrate on implicit inputs during a real-world Wizard of Oz experiment using our human-robot working cell. We show that hand positions can be used to anticipate user needs in a semi-structured environment by applying knowledge about the semi-structured human behaviour which is distributed over working space and time in a typical manner.

Camera-Projector Calibration - Methods, Influencing Factors and Evaluation Using a Robot and Structured-Light 3D Reconstruction

As camera and projector hardware gets more and more affordable and software algorithms more sophisticated, the area of application for camera-projector configurations widens its scope. Unlike for sole camera calibration, only few comparative surveys for projector calibration methods exist. Therefore, in this paper, two readily available algorithms for the calibration of those arrays are studied and methods for the evaluation of the results are proposed. Additionally, statistical evaluations under consideration of different influencing factors like the hardware arrangement, the number of input images or the calibration target characteristics on the accuracy of the calibration results are performed. Ground truth comparison data is realized through a robotic system and structured light 3D scanning.

A Systematic Approach for the Parameterisation of the Kernel-Based Hough Transform Using a Human-Generated Ground Truth

Lines are one of the basic features that are used to characterise the content of an image and to detect objects. Unlike edges or segmented blobs, lines are not only an accumulation of certain feature pixels but can also be described in an easy and exact mathematical way. Besides a lot of different detection methods, the Hough transform has gained much attention in recent years. With increasing processing power and continuous development, computer vision algorithms get more powerful with respect to speed, robustness and accuracy. But there still arise problems when searching for the best parameters for an algorithm or when characterising and evaluating the results of feature detection tasks. It is often difficult to estimate the accuracy of an algorithm and the influences of the parameter selection. Highly interdependent parameters and preprocessing steps continually lead to only hardly comprehensible results. Therefore, instead of pure trial and error and subjective ratings, a systematic assessment with a hard, numerical evaluation criterion is suggested. The paper at hand deals with the latter ones by using a human-generated ground truth to approach the problem. Thereby, the accuracy of the surveyed Kernel-based Hough transform algorithm was improved by a factor of three. These results are used for the tracking of cylindrical markers and to reconstruct their spatial arrangement for a biomedical research application.

Least squares pose estimation of cylinder axes from multiple views using contour line features

In this paper, a new method for a minimum-error pose estimation of cylinder axes based on apparent contour line features from multiple views is presented. Novel model equations for both single and particularly multiple views are derived, and based upon these, an iterative algorithm for least squares fitting the model to imaged cylinder contour line features is introduced. The good performance and fast convergence of the proposed algorithm is shown by solving exemplary fitting problems.