Collaborative robots are increasingly common in modern production systems, since they allow to merge the productivity of automated systems with the flexibility and dexterity of manual ones. The direct interaction between the human and the robot can be the greatest advantage and the greatest limit of collaborative systems at the same time, depending on how it affects human factors like ergonomics and mental stress. This work presents an overview of collaborative robotics considering three main dimensions: robot features, modern production systems characteristics and human factors. A literature review on how such dimensions interact is addressed and a discussion on the current state of the art is presented, showing the topics that have been already widely explored and the research gaps that should be fulfilled in the future.
New technologies, such as collaborative robots, are an option to improve productivity and flexibility in assembly systems. Task allocation is fundamental to properly assign the available resources. However, safety is usually not considered in the task allocation for assembly systems, even if it is fundamental to ensure the safety of human operator when he/she is working with the cobot. Hence, a model that considers safety as a constraint is here presented, with the aim to both maximize the productivity in a collaborative workcell and to promote a secure human robot collaboration. Indexes that consider both process and product characteristics are considered to evaluate the quality of the proposed model, which is also compared with one without the safety constraint. The results confirm the validity and necessity of the newly proposed method, which ensures the safety of the operator while improving the performance of the system.
The migration from Industry 4.0 to Industry 5.0 is becoming more relevant nowadays, with a consequent increase in interest in the operators’ wellness in their working environment. In modern industry, there are different activities that require the flexibility of human operators in performing different tasks, while some others can be performed by collaborative robots (cobots), which promote a fair division of the tasks among the resources in industrial applications. Initially, these robots were used to increase productivity, in particular in assembly systems; currently, new goals have been introduced, such as reducing operator’s fatigue, so that he/she can be more effective in the tasks that require his/her flexibility. For this purpose, a model that aims to realize a multi-objective optimization for task allocation is here proposed. It includes makespan minimization, but also the operator’s energy expenditure and average mental workload reduction. The first objective is to reach the required high productivity standards, while the latter is to realize a human-centered workplace, as required by the Industry 5.0 paradigms. A method for average mental workload evaluation in the entire assembly process and a new constraint, related to resources’ idleness, are here suggested, together with the evaluation of the methodology in a real case study. The results show that it is possible to combine all these elements finding a procedure to define the optimal task allocation that improves the performance of the systems, both for efficiency and for workers’ well-being.
Parts feeding is a complex logistic problem that is further complicated by the market demand for more product variety, which forces companies and manufacturers to adopt the mixed model approach in their assembly systems. Among the parts feeding policies for mixed-model assembly systems, there is the so-called “station-sequence” policy, where stationary kits are prepared using sequences of parts that follow the sequence of the production models. This policy can reduce stocks at the assembly stations but can also lead to potential production stops due to its low robustness. The aim of this paper is to study the station-sequence parts feeding policy, focusing on its dynamic time dependence and analyzing the effects of time and model mix perturbations on the performance of the assembly system. The study was conducted through a simulation model and a statistical analysis. The final discussion also provides a set of Industry 4.0 (I4.0) enabled solutions that are able to address the negative effect of variability on the performance of the system.
Nowadays, the current market trend is oriented toward increasing mass customization, meaning that modern production systems have to be able to be flexible but also highly productive. This is due to the fact that we are still living in the so-called Industry 4.0, with its cornerstone of high-productivity systems. However, there is also a migration toward Industry 5.0 that includes the human-centered design of the workplace as one of its principles. This means that the operators have to be put in the center of the design techniques in order to maximize their wellness. Among the wide set of new technologies, collaborative robots (cobots) represent one such technology that modern production systems are trying to integrate, because of their characteristic of working directly with the human operators, allowing for a mix of the flexibility of the manual systems with the productivity of the automated ones. This paper focuses on the impact that these technologies have on different levels within a production plant and on the improvement of the collaborative experience. At the workstation level, the control methodologies are investigated and developed: technologies such as computer vision and augmented reality can be applied to aid and guide the activities of the cobot, in order to obtain the following results. The first is an increase in the overall productivity generated by the reduction of idle times and safety stops and the minimization of the effort required to the operator during the work. This can be achieved through a multiobjective task allocation which aims to simultaneoulsy minimize the makespan, for productivity requirements, and the operator’s energy expenditure and mental workload, for wellness requirements. The second is a safe, human-centered, workspace in which collisions can be avoided in real time. This can be achieved by using real-time multicamera systems and skeleton tracking to constantly know where the operator is in the work cell. The system will offer the possibility of directing feedback based on the discrepancies between the physical world and the virtual models in order to dynamically reallocate the tasks to the resources if the requirements are not satisfied anymore. This allows the application of the technology to sectors that require constant process control, improving also the human–robot interaction: the human operator and the cobot are not merely two single resources working in the same cell, but they can achieve a real human–robot collaboration. In this paper, a framework is preented that allows us to reach the different aforementioned goals.
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