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Chlorobenzenes are important starting materials for the preparation of commercially valuable triarylphosphines and tetraarylphosphonium salts, but their use for the direct arylation of elemental phosphorus has been elusive. Here we...

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This paper introduces the KUKA Robot Learning Lab at KIT -a remotely accessible robotics testbed. The motivation behind the laboratory is to make state-of-the-art industrial lightweight robots more accessible for education and research. Such expensive hardware is usually not available to students or less privileged researchers to conduct experiments. This paper describes the design and operation of the Robot Learning Lab and discusses the challenges that one faces when making experimental robot cells remotely accessible. Especially safety and security must be ensured, while giving users as much freedom as possible when developing programs to control the robots. A fully automated and efficient processing pipeline for experiments makes the lab suitable for a large amount of users and allows a high usage rate of the robots.

The Internet of Things (IoT) as well as many other new applications require sensors that can already process data inside the sensor and exchange the pre-processed data more or less directly with their environment. Such sensors typically have a digital output and thus challenge current calibration systems which usually have analogue input channels. Furthermore most calibration standards were written for an analogue world and do not fit to sensors with internal A/D converters and data pre-processing. Based on experiences of the authors with the calibration of accelerometers with digital output, the paper will give an overview over the challenges that we will face in a digital sensor world. How will calibration systems for such transducers will look like? How do I calculate a measurement uncertainty if the signal processing inside a sensor is a black box? The paper addresses the challenges and tries to give an outlook how to meet them.