Instrumentation & Measurement Magazine 23-2 - 50

Fig. 3. Illustration of the system connectivity with an NCAP in an agile development process of wireless sensors using Transducer Electronic Datasheets (TEDS).

becomes easy and thus, this allows for an agile approach. During this process, the TEDS gets more and more specific and the
final step from the prototype to a productive system becomes
small, the development time reduces and the final results
match the actual user needs.
Based on an initial TEDS, the NCAP automatically generates a virtual transducer, which can already be accessed as
any other transducer connected to the NCAP, but initially
only provides random data, yet in accordance with the TEDS.
The NCAP also provides a template/API to connect to simulated data, which can range from simple signal generation or
playback from files to advanced real time system simulations.
Laboratory prototypes can be connected in the same way as
simulations. Finally, a productive sensor or transducer connects to the NCAP in the usual way, e.g. using an IEEE 1451.4
protocol. As virtual and real transducers are accessed in the
same way, this allows that the interfaces and software in the
automation system developed and tested based on the initial virtual transducer do not change when cycled between
50	

simulation in the loop, prototype in the loop, etc. and ideally
do not require any further modifications of the automation
software due to communication issues, data format, etc.

Use Case Example-Robot Sensing in
Industrial Environment
Our work on the approach and our current implementation
of the NCAP with functionality according to Fig. 1 is based on
several use cases, ranging from scientific experiments, condition monitoring and industrial test benches to robot sensing.
One such use case is within a research project called AutoLOG-Automatic log ordering through robotic grasping. The
project focuses on research towards automating challenging and
DDD (dull, dangerous, dirty) tasks concerning the handling of
raw material in production lines currently executed manually.
Besides artificial intelligence inspired vision-based approaches
to categorize and segment the raw material and its geometry to subsequently define optimal handling/grasping poses
for the automation machinery and robust control strategies,

IEEE Instrumentation & Measurement Magazine	

April 2020



Instrumentation & Measurement Magazine 23-2

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