Human-Robot Collaboration: towards a human-centered approach in manufacturing

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An experimental focus on learning effect and interaction quality in human–robot collaboration

In the landscape of the emerging Industry 5.0, human–robot collaboration (HRC) represents a solution to increase the fex ibility and reconfgurability of production processes. Unlike classical industrial automation, in HRC it is possible to have direct interaction between humans and robots. Consequently, in order to efectively implement HRC it is necessary to con sider not only technical aspects related to the robot but also human aspects. The focus of this paper is to expand on previous results investigating how the learning process (i.e., the experience gained through the interaction) afects the user experience in the HRC in conjunction with diferent confguration factors (i.e., robot speed, task execution control, and proximity to robot workspace). Participants performed an assembly task in 12 diferent confgurations and provided feedback on their experience. In addition to perceived interaction quality, self-reported afective state and stress-related physiological indica tors (i.e., average skin conductance response and heart rate variability) were collected. A deep quantitative analysis of the response variables revealed a signifcant infuence of the learning process in the user experience. In addition, the perception of some confguration factors changed during the experiment. Finally, a signifcant infuence of participant characteristics also emerged, auguring the necessity of promoting a human-centered HRC

Manual assembly and Human–Robot Collaboration in repetitive assembly processes: a structured comparison based on human‑centered performances

Human–Robot Collaboration (HRC) represents an innovative solution able to enhance quality and adaptability of production processes. However, to fully exploit the benefts of HRC, human factors must be also taken into account. A novel experimental setting involving a repetitive assembly process is presented to investigate the efects of prolonged HRC on user experience and performance. Each participant was involved in two 4-h shifts: a manual assembly setting and a HRC one. The response variables collected in the study included self-reported afective state, perceived body discomfort, perceived workload, physiological signals for stress (i.e., heart rate variability and electrodermal activity), process and product defectiveness. Experimental results showed less upper limb exertion in the HRC setting, emphasizing the contribution of cobots in improving physical ergonomics in repetitive processes. Furthermore, results showed reduced mental efort, stress, and fewer process defects in the HRC setting, highlighting how collaborative robotics can improve process quality by supporting operators from a cognitive point of view in repetitive processes

Applications of Affective Computing in Human-Robot Interaction: state-of-art and challenges for manufacturing

The introduction of collaborative robots aims to make production more flexible, promoting a greater interaction between humans and robots also from physical point of view. However, working closely with a robot may lead to the creation of stressful situations for the operator, which can negatively affect task performance. In Human-Robot Interaction (HRI), robots are expected to be socially intelligent, i.e., capable of understanding and reacting accordingly to human social and affective clues. This ability can be exploited implementing affective computing, which concerns the development of systems able to recognize, interpret, process, and simulate human affects. Social intelligence is essential for robots to establish a natural interaction with people in several contexts, including the manufacturing sector with the emergence of Industry 5.0. In order to take full advantage of the human-robot collaboration, the robotic system should be able to perceive the psycho-emotional and mental state of the operator through different sensing modalities (e.g., facial expressions, body language, voice, or physiological signals) and to adapt its behaviour accordingly. The development of socially intelligent collaborative robots in the manufacturing sector can lead to a symbiotic human-robot collaboration, arising several research challenges that still need to be addressed. The goals of this paper are the following: (i) providing an overview of affective computing implementation in HRI; (ii) analyzing the state-of-art on this topic in different application contexts (e.g., healthcare, service applications, and manufacturing); (iii) highlighting research challenges for the manufacturing sector

Comparing quality profiles in Human-Robot Collaboration: empirical evidence in the automotive sector

Purpose: Human-Robot Collaboration (HRC) is a paradigm that is gradually consolidating in the industrial field. The goal of this paradigm is to combine human and robot skills to make production more flexible. An effective implementation of HRC requires a careful analysis of its different aspects, related to both robots and humans. For this reason, the development of a tool able to consider all HRC aspects to evaluate the collaboration quality is a real practical need. Design/methodology/approach: In a previous work, Gervasi et al. (2020) proposed a multidimensional framework to evaluate HRC quality. This framework has been tested on a real industrial HRC application in the automotive sector. Two different alternatives of the same assembly task were analyzed and compared on the quality reference framework. Findings: The comparison between the two alternatives of the same assembly task highlighted the framework's ability to detect the effects of different configurations on the various HRC dimensions. This ability can be useful in decision making processes and in improving the collaboration quality. Social implications: The framework considers the human aspects related to the interaction with robots, allowing to effectively monitor and improve the collaboration quality and operator satisfaction. Originality/value: This paper extends and shows the use of the HRC evaluation framework proposed by Gervasi et al. (2020) on real industrial applications. In addition, an HRC application implemented in an important automotive company is described and analyzed in detail

Cancer data quality and harmonization in Europe: the experience of the BENCHISTA Project – international benchmarking of childhood cancer survival by stage

IntroductionVariation in stage at diagnosis of childhood cancers (CC) may explain differences in survival rates observed across geographical regions. The BENCHISTA project aims to understand these differences and to encourage the application of the Toronto Staging Guidelines (TG) by Population-Based Cancer Registries (PBCRs) to the most common solid paediatric cancers.MethodsPBCRs within and outside Europe were invited to participate and identify all cases of Neuroblastoma, Wilms Tumour, Medulloblastoma, Ewing Sarcoma, Rhabdomyosarcoma and Osteosarcoma diagnosed in a consecutive three-year period (2014-2017) and apply TG at diagnosis. Other non-stage prognostic factors, treatment, progression/recurrence, and cause of death information were collected as optional variables. A minimum of three-year follow-up was required. To standardise TG application by PBCRs, on-line workshops led by six tumour-specific clinical experts were held. To understand the role of data availability and quality, a survey focused on data collection/sharing processes and a quality assurance exercise were generated. To support data harmonization and query resolution a dedicated email and a question-and-answers bank were created.Results67 PBCRs from 28 countries participated and provided a maximally de-personalized, patient-level dataset. For 26 PBCRs, data format and ethical approval obtained by the two sponsoring institutions (UCL and INT) was sufficient for data sharing. 41 participating PBCRs required a Data Transfer Agreement (DTA) to comply with data protection regulations. Due to heterogeneity found in legal aspects, 18 months were spent on finalizing the DTA. The data collection survey was answered by 68 respondents from 63 PBCRs; 44% of them confirmed the ability to re-consult a clinician in cases where stage ascertainment was difficult/uncertain. Of the total participating PBCRs, 75% completed the staging quality assurance exercise, with a median correct answer proportion of 92% [range: 70% (rhabdomyosarcoma) to 100% (Wilms tumour)].ConclusionDifferences in interpretation and processes required to harmonize general data protection regulations across countries were encountered causing delays in data transfer. Despite challenges, the BENCHISTA Project has established a large collaboration between PBCRs and clinicians to collect detailed and standardised TG at a population-level enhancing the understanding of the reasons for variation in overall survival rates for CC, stimulate research and improve national/regional child health plans

Human-robot collaboration in a repetitive assembly process: a preliminary investigation on operator’s experience and product quality outputs.

Purpose – Human-Robot Collaboration (HRC) aims to combine the skills of humans with those of robots, representing a solution to increase the quality and reconfigurability of manufacturing processes. However, to fully exploit the benefits of HRC, human factors, including the operator's psychological well-being, must be considered. To this end, this paper proposes an experimental setting aimed at exploring human-related aspects during HRC. Design/methodology/approach – In order to explore the effects of prolonged HRC in a repetitive assembly process, a novel experimental setup concerning the production process of a tile cutter is proposed. Each participant is asked to perform three assembly shifts: two in collaborative mode with cobot support and one in manual mode. The response variables collected in the study include the quality of the interaction performed, workload, affective state of the operator and physiological indicators of stress (heart rate variability and electrodermal activity). Process defectiveness is also tracked. Findings – Preliminary results show that HRC sessions tend to generate more stress than manual assembly sessions. However, increasing familiarity with the collaborative task tends to reduce this effect. These results are confirmed by both subjective and physiological responses. Research limitations/implications – The evidence for the results found is limited by the number of participants involved. An experimental campaign with a larger number of participants is needed to confirm the preliminary findings. Originality/value – This paper proposes a novel experimental study aimed at recreating a work shift in a collaborative assembly workstation of a production process. This experimental setting draws attention to the need to investigate the implications of prolonged HRC. In addition, a non-invasive biosensor is implemented to investigate the state of humans during HRC

Towards the definition of a Human-Robot collaboration scale

Human-Robot Collaboration (HRC) is a form of direct interaction between humans and robots. The aim of this form of interaction is to complete a specific task combining the abilities of both the human and the robots. HRC is characterized by many different aspects. Many works have focused on the evaluation of specific aspects related to HRC, e.g. safety, trust, etc. However, a major issue is to find a general framework to evaluate the collaboration between humans and robots considering all the aspects of the problem. The main goal of this paper is to highlight the multiple dimensions that characterize the HRC problem

A conceptual framework to evaluate human-robot collaboration

Human-Robot Collaboration (HRC) is a form of direct interaction between humans and robots. The objective of this type of interaction is to perform a task by combining the skills of both humans and robots. HRC is characterized by several aspects, related both to robots and humans. Many works have focused on the study of specific aspects related to HRC, e.g., safety, task organization. However, a major issue is to find a general framework to evaluate the collaboration between humans and robots considering all the aspects of the interaction. The goals of this paper are the following: (i) highlighting the different latent dimensions that characterize the HRC problem and (ii) constructing a conceptual framework to evaluate and compare different HRC configuration profiles. The description of the methodology is supported by some practical examples

User experience and physiological response in Human-Robot Collaboration: a preliminary investigation

Within the context of Industry 4.0 and of the new emerging Industry 5.0, human factors are becoming increasingly important, especially in Human-Robot Collaboration (HRC). This paper provides a novel study focused on the human aspects involved in industrial HRC by exploring the efects of various HRC setting factors. In particular, this paper aims at investigating the impact of industrial HRC on user experience, afective state, and stress, assessed through both subjective measures (i.e., questionnaires) and objective ones (i.e., physiological signals). A collaborative assembly task was implemented with difer ent confgurations, in which the robot movement speed, the distance between the operator and the robot workspace, and the control of the task execution time were varied. Forty-two participants were involved in the study and provided feedbacks on interaction quality and their afective state. Participants’ physiological responses (i.e., electrodermal activity and heart rate) were also collected non-invasively to monitor the amount of stress generated by the interaction. Analysis of both subjective and objective responses revealed how the confguration factors considered infuence them. Robot movement speed and control of the task execution time resulted to be the most infuential factors. The results also showed the need for customization of HRC to improve ergonomics, both psychological and physical, and the well-being of the operator