The implementation of smart technologies and physical collaboration with robots in manufacturing can provide competitive advantages in production, performance and quality, as well as improve working conditions for operators. Due to the rapid advancement of smart technologies and robot capabilities, operators face complex task processes, decline in competences due to robots overtaking tasks, and reduced learning opportunities, as the range of tasks that they are asked to perform is narrower. The Industry 5.0 framework introduced, among others, the human-centric workplace, promoting operators wellbeing and use of smart technologies and robots to support them. This new human centric framework enables operators to learn new skills and improve their competencies. However, the need to understand the effects of the workplace changes remain, especially in the case of human robot collaboration, due to the dynamic nature of human robot interaction. A literature review was performed, initially, to map the effects of workplace changes on operators and their capabilities. Operators need to perform tasks in a complex environment in collaboration with robots, receive information from sensors or other means (e.g. through augmented reality glasses) and decide whether to act upon them. Meanwhile, operators need to maintain their productivity and performance. This affects cognitive load and fatigue, which increases safety risks and probability of human-system error. A model for error probability was formulated and tested in collaborative scenarios, which regards the operators as natural systems in the workplace environment, taking into account their condition based on four macro states; behavioural, mental, physical and psychosocial. A scoping review was then performed to investigate the robot design features effects on operators in the human robot interaction system. Here, the outcomes of robot design features effects on operators were mapped and potential guidelines for design purposes were identified. The results of the scoping review showed that, apart from cognitive load, operators perception on robots reliability and their safety, along with comfort can influence team cohesion and quality in the human robot interaction system. From the findings of the reviews, an experimental study was designed with the support of the industrial partner. The main hypothesis was that cognitive load, due to collaboration, is correlated with quality of product, process and human work. In this experimental study, participants had to perform two tasks; a collaborative assembly and a secondary manual assembly. Perceived task complexity and cognitive load were measured through questionnaires, and quality was measured through errors participants made during the experiment. Evaluation results showed that while collaboration had positive influence in performing the tasks, cognitive load increased and the temporal factor was the main reason behind the issues participants faced, as it slowed task management and decision making of participants. Potential solutions were identified that can be applied to industrial settings, such as involving participants/operators in the task and workplace design phase, sufficient training with their robot co-worker to learn the task procedures and implement direct communication methods between operator and robot for efficient collaboration.

Human centric collaborative workplace: the human robot interaction system perspective

PANAGOV, SOTIRIOS
2023

Abstract

The implementation of smart technologies and physical collaboration with robots in manufacturing can provide competitive advantages in production, performance and quality, as well as improve working conditions for operators. Due to the rapid advancement of smart technologies and robot capabilities, operators face complex task processes, decline in competences due to robots overtaking tasks, and reduced learning opportunities, as the range of tasks that they are asked to perform is narrower. The Industry 5.0 framework introduced, among others, the human-centric workplace, promoting operators wellbeing and use of smart technologies and robots to support them. This new human centric framework enables operators to learn new skills and improve their competencies. However, the need to understand the effects of the workplace changes remain, especially in the case of human robot collaboration, due to the dynamic nature of human robot interaction. A literature review was performed, initially, to map the effects of workplace changes on operators and their capabilities. Operators need to perform tasks in a complex environment in collaboration with robots, receive information from sensors or other means (e.g. through augmented reality glasses) and decide whether to act upon them. Meanwhile, operators need to maintain their productivity and performance. This affects cognitive load and fatigue, which increases safety risks and probability of human-system error. A model for error probability was formulated and tested in collaborative scenarios, which regards the operators as natural systems in the workplace environment, taking into account their condition based on four macro states; behavioural, mental, physical and psychosocial. A scoping review was then performed to investigate the robot design features effects on operators in the human robot interaction system. Here, the outcomes of robot design features effects on operators were mapped and potential guidelines for design purposes were identified. The results of the scoping review showed that, apart from cognitive load, operators perception on robots reliability and their safety, along with comfort can influence team cohesion and quality in the human robot interaction system. From the findings of the reviews, an experimental study was designed with the support of the industrial partner. The main hypothesis was that cognitive load, due to collaboration, is correlated with quality of product, process and human work. In this experimental study, participants had to perform two tasks; a collaborative assembly and a secondary manual assembly. Perceived task complexity and cognitive load were measured through questionnaires, and quality was measured through errors participants made during the experiment. Evaluation results showed that while collaboration had positive influence in performing the tasks, cognitive load increased and the temporal factor was the main reason behind the issues participants faced, as it slowed task management and decision making of participants. Potential solutions were identified that can be applied to industrial settings, such as involving participants/operators in the task and workplace design phase, sufficient training with their robot co-worker to learn the task procedures and implement direct communication methods between operator and robot for efficient collaboration.
22-mar-2023
Inglese
FRUGGIERO, FABIO
Università degli studi della Basilicata
Inglese
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/65895
Il codice NBN di questa tesi è URN:NBN:IT:UNIBAS-65895