Fachbereich Elektrotechnik und Informationstechnik
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In the future, we expect manufacturing companies to follow a new paradigm that mandates more automation and autonomy in production processes. Such smart factories will offer a variety of production technologies as services that can be combined ad hoc to produce a large number of different product types and variants cost-effectively even in small lot sizes. This is enabled by cyber-physical systems that feature flexible automated planning methods for production scheduling, execution control, and in-factory logistics.
During development, testbeds are required to determine the applicability of integrated systems in such scenarios. Furthermore, benchmarks are needed to quantify and compare system performance in these industry-inspired scenarios at a comprehensible and manageable size which is, at the same time, complex enough to yield meaningful results.
In this chapter, based on our experience in the RoboCup Logistics League (RCLL) as a specific example, we derive a generic blueprint for how a holistic benchmark can be developed, which combines a specific scenario with a set of key performance indicators as metrics to evaluate the overall integrated system and its components.
Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure.
This paper introduces a hardware setup to measure efficiency maps of low-power electric motors and their associated inverters. Here, the power of the device under test (DUT) ranges from some Watts to a few hundred Watts. The torque and speed of the DUT are measured independent of voltage and current in multiple load points. A Matlab-based software approach in combination with an open Texas-Instruments (TI) hardware setup ensures flexibility. Exemplarily, the efficiency field of a Permanent Magnet Synchronous Machine (PMSM) is measured to proof the concept. Brushless-DC (BLDC) motors can be tested as well. The nomenclature in this paper is based on the new European standard DIN EN 50598. Special attention is paid to the calculation of the measurement error.