Flow in human-robot collaboration-multimodal analysis and perceived challenge detection in industrial scenarios.

Introduction: Flow state, the optimal experience resulting from the equilibrium between perceived challenge and skill level, has been extensively studied in various domains. However, its occurrence in industrial settings has remained relatively unexplored. Notably, the literature predominantly focuses on Flow within mentally demanding tasks, which differ significantly from industrial tasks. Consequently, our understanding of emotional and physiological responses to varying challenge levels, specifically in the context of industry-like tasks, remains limited. Methods: To bridge this gap, we investigate how facial emotion estimation (valence, arousal) and Heart Rate Variability (HRV) features vary with the perceived challenge levels during industrial assembly tasks. Our study involves an assembly scenario that simulates an industrial human-robot collaboration task with three distinct challenge levels. As part of our study, we collected video, electrocardiogram (ECG), and NASA-TLX questionnaire data from 37 participants. Results: Our results demonstrate a significant difference in mean arousal and heart rate between the low-challenge (Boredom) condition and the other conditions. We also found a noticeable trend-level difference in mean heart rate between the adaptive (Flow) and high-challenge (Anxiety) conditions. Similar differences were also observed in a few other temporal HRV features like Mean NN and Triangular index. Considering the characteristics of typical industrial assembly tasks, we aim to facilitate Flow by detecting and balancing the perceived challenge levels. Leveraging our analysis results, we developed an HRV-based machine learning model for discerning perceived challenge levels, distinguishing between low and higher-challenge conditions. Discussion: This work deepens our understanding of emotional and physiological responses to perceived challenge levels in industrial contexts and provides valuable insights for the design of adaptive work environments.

Behavioral patterns in robotic collaborative assembly: comparing neurotypical and Autism Spectrum Disorder participants.

Introduction: In Industry 4.0, collaborative tasks often involve operators working with collaborative robots (cobots) in shared workspaces. Many aspects of the operator's well-being within this environment still need in-depth research. Moreover, these aspects are expected to differ between neurotypical (NT) and Autism Spectrum Disorder (ASD) operators. Methods: This study examines behavioral patterns in 16 participants (eight neurotypical, eight with high-functioning ASD) during an assembly task in an industry-like lab-based robotic collaborative cell, enabling the detection of potential risks to their well-being during industrial human-robot collaboration. Each participant worked on the task for five consecutive days, 3.5 h per day. During these sessions, six video clips of 10 min each were recorded for each participant. The videos were used to extract quantitative behavioral data using the NOVA annotation tool and analyzed qualitatively using an ad-hoc observational grid. Also, during the work sessions, the researchers took unstructured notes of the observed behaviors that were analyzed qualitatively. Results: The two groups differ mainly regarding behavior (e.g., prioritizing the robot partner, gaze patterns, facial expressions, multi-tasking, and personal space), adaptation to the task over time, and the resulting overall performance. Discussion: This result confirms that NT and ASD participants in a collaborative shared workspace have different needs and that the working experience should be tailored depending on the end-user's characteristics. The findings of this study represent a starting point for further efforts to promote well-being in the workplace. To the best of our knowledge, this is the first work comparing NT and ASD participants in a collaborative industrial scenario.

Assessing the Relationships between Interdigital Geometry Quality and Inkjet Printing Parameters.

Drop on demand (DoD) inkjet printing is a high precision, non-contact, and maskless additive manufacturing technique employed in producing high-precision micrometer-scaled geometries allowing free design manufacturing for flexible devices and printed electronics. A lot of studies exist regarding the ink droplet delivery from the nozzle to the substrate and the jet fluid dynamics, but the literature lacks systematic approaches dealing with the relationship between process parameters and geometrical outcome. This study investigates the influence of the main printing parameters (namely, the spacing between subsequent drops deposited on the substrate, the printing speed, and the nozzle temperature) on the accuracy of a representative geometry consisting of two interdigitated comb-shape electrodes. The study objective was achieved thanks to a proper experimental campaign developed according to Design of Experiments (DoE) methodology. The printing process performance was evaluated by suitable geometrical quantities extracted from the acquired images of the printed samples using a MATLAB algorithm. A drop spacing of 140 µm and 170 µm on the two main directions of the printing plane, with a nozzle temperature of 35 °C, resulted as the most appropriate parameter combination for printing the target geometry. No significant influence of the printing speed on the process outcomes was found, thus choosing the highest speed value within the investigated range can increase productivity.

Modelling and testing of a wave energy converter based on dielectric elastomer generators.

This paper introduces the analysis and design of a wave energy converter (WEC) that is equipped with a novel kind of electrostatic power take-off system, known as dielectric elastomer generator (DEG). We propose a modelling approach which relies on the combination of nonlinear potential-flow hydrodynamics and electro-hyperelastic theory. Such a model makes it possible to predict the system response in operational conditions, and thus it is employed to design and evaluate a DEG-based WEC that features an effective dynamic response. The model is validated through the design and test of a small-scale prototype, whose dynamics is tuned with waves at tank-scale using a set of scaling rules for the DEG dimensions introduced here in order to comply with Froude similarity laws. Wave-tank tests are conducted in regular and irregular waves with a functional DEG system that is controlled using a realistic prediction-free strategy. Remarkable average performance in realistically scaled sea states has been recorded during experiments, with peaks of power output of up to 3.8 W, corresponding to hundreds of kilowatts at full-scale. The obtained results demonstrated the concrete possibility of designing DEG-based WEC devices that are conceived for large-scale electrical energy production.

Fabrication and Test of an Inflated Circular Diaphragm Dielectric Elastomer Generator Based on PDMS Rubber Composite.

This paper introduces a fabrication method and the experimental characterization of a soft polymeric energy converter manufactured using a combination of dielectric and conductive polydimethylsiloxane elastomers. The presented system is an inflated circular diaphragm dielectric elastomer generator; i.e., a deformable electrostatic transducer that converts the mechanical work done by a time-varying pressure into electricity. A prototype of the system is realized on the basis of a simple fabrication procedure that makes use of commercially available silicone dielectric elastomer films and custom-prepared deformable conductive electrodes. A test-bench is developed and employed to estimate the energy conversion performance. Remarkable results are obtained, such as an amount of energy converted per cycle of up to 0.3 J, converted power of up to 0.15 W, energy per unit of employed elastomer mass of up to 173 J/kg, and fraction of the input mechanical work converted into electricity of 30%.