One of the key interesting features of collaborative robotic applications is the potential to lighten the worker workload and potentiate better working conditions. Moreover, developing robotics applications that meets ergonomic criteria is not always a straightforward endeavor. We propose a framework to guide the safe design and conceptualization of ergonomic-driven collaborative robotics workstations. A multi-disciplinary approach involving robotics and ergonomics and human factors shaped this methodology that leads future engineers through the digital transformation of a manual assembly (with repetitive and hazardous operations) to a hybrid workstation, focusing on the physical ergonomic improvement. The framework follows four main steps, (i) the characterization of the initial condition, (ii) the risk assessment, (iii) the definition of requirements for a safe design, and (iv) the conceptualization of the hybrid workstation with all the normative implications it entails. We applied this methodology to a case study in an assembly workstation of a furniture manufacturing company. Results show that the methodology adopted sets an adequate foundation to accelerate the design and development of new human-centered collaborative robotic workstations.

In environments shared with humans, Autonomous Mobile Robots (AMRs) should be designed with human-aware motion-planning skills. Even when AMRs can effectively avoid humans, only a handful of studies have evaluated the human perception of mobile robots. To establish appropriate non-verbal communication, robot movement should be legible and should consider the human element. In this paper, a study that evaluates humans’ perceptions of different AMR courtesy behaviors at industrial facilities, particularly at crossing areas, is presented. To evaluate the proposed kinesic courtesy cues, we proposed five tests (four proposed cues—stop, deceleration, retreating, and retreating and moving aside—and one control test) with a set of participants taken two by two. We assessed three different metrics, namely, the participants’ self-reported trust in AMR behavior, the legibility of the courtesy cues in the participants’ opinions, and the behavioral analysis of the participants related to each courtesy cue tested. The retreating courtesy cue, regarding the legibility of the AMR behavior, and the decelerate courtesy cue, regarding the behavioral analysis of the participants’ signs of hesitation, are better perceived from the forward view. The results obtained regarding the participants’ self-reported trust showed no significant differences in the two participant perspectives.

As manufacturing enters an era of mass customization, new technological solutions with more versatility and flexibility are brought to the industry. Collaborative robots, or COBOTs, can now safely work near humans, a key advantage that enables highly-flexible production and opens the possibility to tackle the incidence of occupational health problems. Although the advantages of collaborative robotic manufacturing have been extensively praised, adapting a manual production workstation to integrate robotic technology is far from being a linear and easy process. In this work, we explore this delicate balance between the ergonomic, production, and safety requirements in the design of a new collaborative cell. We explain the different steps of this digital transformation process and the thought process behind human-robot task allocation to maximize the collaborative robot efficiency while improving the work conditions. This work describes the development of a certified and production-ready collaborative workstation based on ergonomic criteria. Results show a significant reduction in the global ergonomic risk score associated with the workers' actions and postures and an increase in production quotas, compared to the previous manual process.

Dynamic Neural Fields (DNFs) offer a biologically grounded framework for modelling continuous-time neural dynamics underlying a wide range of cognitive and sensorimotor functions. Despite their expressive power, DNF architectures are typically designed and tuned manually, as their nonlinear dynamics and kernel-based interactions make systematic design and parameterisation difficult. As a result, constructing functional DNF models requires expert knowledge and iterative trial-and-error. We introduce NEAT-DNFs, a neuroevolutionary framework that extends NeuroEvolution of Augmenting Topologies (NEAT) to the automated synthesis of DNF architectures. In NEAT-DNFs, neural fields and inter-field interactions are encoded as evolvable genes, enabling the joint evolution of intrinsic parameters and architectural topology. This allows continuous-time neural dynamics to be discovered autonomously, without manual tuning. We evaluate NEAT-DNFs on a hierarchy of benchmark tasks of increasing complexity. Initial tasks target core Dynamic Field Theory (DFT) mechanisms, including detection, working memory, and selection. We then consider more demanding problems that require the emergence of intermediate neural fields, thereby testing the framework's capacity for structural innovation. Across all tasks, NEAT-DNFs reliably evolves compact and stable architectures, increasing structural complexity only when necessary. This work bridges neuroevolution and DFT, establishing a foundation for the automated design of biologically inspired neural field models.