Not only large companies, but also small ones are moving toward process automation, due to the need to improve mental and physical well-being of the workers. Human-centred manufacturing is crucial to increase flexibility, agility and competitiveness (Lu et al., 2022). Humans are no longer meant to do tedious and strenuous work, but to add high-value content to final products by exploiting their cognitive abilities. The relationship between humans and machines has radically changed with the transformation of Industry 5.0: machines are required to perceive and adapt to workers' needs, not vice versa. Production is changing in all companies in different sectors, even in those that have production characteristics totally opposed to an automated process. For example, in footwear luxury industries the processes are typically manually performed by expert artisans due to high quality, precision and comfort requirements of products and to the wide variability and customisation of production. All these features are in contrast to full automation, and only craft work can bring profitability to the company. However, the need for automation is strong to ensure high productivity but also the well-being of the workers.

The collaboration between humans and robots re-quires a paradigm shift not only in robot perception, reasoning, and action, but also in the design of the robotic cell. This paper proposes an optimization framework for designing collaborative robotics cells using a digital twin during the pre-deployment phase. This approach mitigates the limitations of experience-based sub-optimal designs by means of Bayesian optimization to find the optimal layout after a certain number of iterations. By integrating production KPIs into a black-box optimization frame-work, the digital twin supports data-driven decision-making, reduces the need for costly prototypes, and ensures continuous improvement thanks to the learning nature of the algorithm. The paper presents a case study with preliminary results that show how this methodology can be applied to obtain safer, more efficient, and adaptable human-robot collaborative environments.

The dependence on finite reserves of raw materials and the production of waste are two unsolved problems of the traditional linear economy. Healthcare, as a major sector of any nation, is currently facing them. Hence, in this paper, we report theoretical and practical advances of robotic reprocessing of small medical devices. Specifically, on the theory, we combine compartmental dynamical thermodynamics with the mechanics of robots to integrate robotics into a system-level perspective, and then, propose graph-based circularity indicators by leveraging our thermodynamic framework. Our thermodynamic framework is also a step forward in defining the theoretical foundations of circular material flow designs as it improves material flow analysis (MFA) by adding dynamical energy balances to the usual mass balances. On the practice, we report on the on-going design of a flexible robotic cell enabled by deep-learning vision for resources mapping and quantification, disassembly, and waste sorting of small medical devices.

Our main goal is to show how a robotic cell can withstand In most factories today the robotic cells are deployed on external forces occurring on the move. To achieve well enforced bases to avoid any external impact on the this goal, we take the Agile Robotics for Industrial accuracy of production. In contrast to that, we evaluate Automation Competition (ARIAC) 2018 environment a futuristic concept where the whole robotic cell (ariac2018 2018) as a baseline, and extend it to serve could work in a moving platform. Imagine a trailer of our needs. First, we modified the static environment a truck moving along the motorway while exposed to and mobilized it. The next step was to apply external heavy physical impacts due to maneuvering. The key forces from different sources to the modified model. Our question here is how the robotic cell behaves and how final goal is to examine the productivity changes in the the productivity is affected. We propose a system architecture moving system, and based on the results, propose suggestions (FATHER) and show some solutions including to decrease the impact of the external forces.

Hundreds of industry specialists were recently introduced to APRIL (Automated Processing Robotic Ingredient Loading) at an event organised by automation solutions provider OAL and the University of Lincoln at the UK's National Centre for Food Manufacturing. The session was held to demonstrate to manufacturers what can be achieved using robots to handle ingredients and cook food, so that food manufacturing can become more efficient and safer. "Consumer demand for greater choice and convenience versus the rising cost of food production due to the introduction of the living wage has led food manufacturers to a crossroads," OAL sales and marketing manager Jake Norman told BakeryandSnacks. "These interlinking factors mean the time is now for robotics and automation to deliver flexible food production at the lowest cost." According to Norman, other industries have embraced technology as an alternative to people to great effect.