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urrent market demands require an increasingly agile production environment throughout many manufacturing branches. Traditional automation systems and industrial robots, on the other hand, are often too inflexible to provide an economically viable business case for companies with rapidly changing products. The introduction of cognitive abilities into robotic and automation systems is, therefore, a necessary step toward lean changeover and seamless human-robot collaboration. In this article, we introduce the European Union (EU)funded research project SMErobotics (http://www.smerobotics .org/), which focuses on facilitating the use of robot systems in small and medium-sized enterprises (SMEs). We analyze open challenges for this target audience and develop multiple efficient technologies to address related issues. Realworld demonstrators of several end users and from multiple application domains show the impact these smart robots can have on SMEs. This article intends to give a broad overview of the research conducted in SMErobotics. Specific details of individual topics are provided through references to our previous publications.

Abstract-We present an approach to control a 6 DOF manipulator using an uncalibrated visual servoing (VS) approach that addresses the challenges of choosing proper image features for target objects and designing a VS controller to enhance the tracking performance. The main contribution of this article is the definition of a new virtual visual space (image space). A novel stereo camera model employing virtual orthogonal cameras is used to map 6D poses from Cartesian space to this virtual visual space. Each component of the 6D pose vector defined in this virtual visual space is linearly independent, leading to a full-rank 6 × 6 image Jacobian matrix which allows avoiding classical problems, such as, image space singularities and local minima. Furthermore, the control for rotational and translational motion of robot are decoupled due to the diagonal image Jacobian. Finally, simulation results with an eye-to-hand robotic system confirm the improvement in controller stability and motion performance with respect to conventional VS approaches. Experimental results on a 6 DOF industrial robot are provided to illustrate the effectiveness of the proposed method and the feasibility of using this method in practical scenarios.

Human activity recognition is crucial for intuitive cooperation between humans and robots. We present an approach for activity recognition for applications in the context of humanrobot interaction in industrial settings. The approach is based on spatial and temporal features derived from skeletal data of human workers performing assembly tasks. These features were used to train a machine learning framework, which classifies discrete time frames with Random Forests and subsequently models temporal dependencies between the resulting states with a Hidden Markov Model. We considered the following three groups of activities: Movement, Gestures, and Object handling. A dataset has been collected which is comprised of 24 recordings of several human workers performing such activities in a human-robot interaction environment, as typically seen at small and medium-sized enterprises. The evaluation shows that the approach achieves a recognition accuracy of up to 88% for some activities and an average accuracy of 73%.

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