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Measurement and monitoring systems (MMSs) are intrinsically part of 4.0 and, in particular, of cyber-physical systems (CPSs). However, by introducing the 4.0 enabling technologies into MMSs, also the vice versa can be accomplished, and MMSs can evolve into a cyber-physical measurement system (CPMS). Starting from this consideration, in the present work, a preliminary case study of a CPMS is presented: an innovative robotic platform to be used for measurement systems in confined and constrained remote environments. The proposed system is a soft growing robot that includes a robot base, to be placed outside the remote environments, and a robot body that accesses the site through growth. A pneumatic actuation mechanism enables the controllable growth of the system (through lengthening at its tip), as well as its controllable steering. The system can be equipped with sensors to enable remote monitoring tasks, or can be used to transport sensors in remote locations. The ultimate goal is to achieve a self-adapting, fully-autonomous, reliable and safe system for monitoring applications, particularly useful for the remote inspection of unknown and/or constrained environments.

Advanced measurement systems and techniques from neuroscience are used in this work to extrapolate reduced- order muscle activation patterns corresponding to the execution of overhead tasks classic of automotive industry. The approach is based on the analysis of electromyographic (EMG) signals measured from muscles of the upper limb. The preliminary experiments show that, for the selected tasks, one muscle synergy could account for > 98% of the total muscle activation. This approach might pave the way towards the development of bionic, synergy-based upper limb wearable robots for augmenting human performances in industrial workplaces. © 2019 IEEE.

High accuracy digitalisation of geometric models, related to big size objects, usually is performed by means of multiple acquisitions from different scanning locations. It needs to correctly place the acquired point clouds in 3D digital environment. For this purpose, it is very important identifying the exact scanning location in order to correctly realign point clouds and automatically generate an accurate 3D CAD model. The present paper focuses on design and prototype of a mobile handling device for reverse engineering scanning systems, named Dedalo. It is able to locate itself using a sensor fusion method based on a Kalman Filter. The sensor equipment is composed by wheel encoders and an ultrasonic sensor for measuring the distance from a known reference. Although Dedalo is equipped with low-cost hardware, results have showed a location accuracy by 0,1% error/meter, better than each sensor accuracy.

Simulation results are not representative of a real system behavior up to its model validation. Validation activity needs a model characterization to match real system and model parameters. This activity impacts more on mechatronics systems which are affected by both physical and control characterizations. This work deals with single bowden power window systems; it improves a system model, previously developed, by deepening window aspects and force system. Force types and modules have been obtained by several experimental tests, in different operating conditions. Experimental automated tests have been carried out by means of a new virtual instrument developed in LabVIEW® environment. Comparison of test results highlights three main components related to: sliding friction, window friction and gravity. These components are affected by window shape which induces module variations. We named these variations "window shape effect and these results bring to a new procedure for system validation.