Nowadays, industries are showing a growing interest in human-robot collaboration, particularly for shared tasks. This requires intelligent strategies to plan a robot’s motions, considering both task constraints and human-specific factors such as height and movement preferences. This work introduces a novel approach to generate personalized trajectories using Dynamic Movement Primitives (DMPs), enhanced with real-time velocity scaling based on human feedback. The method was rigorously tested in industrial-grade experiments, focusing on the collaborative transport of an engine cowl lip section. A comparative analysis between DMP-generated trajectories and a standard industrial motion planner (BiTRRT) highlights their adaptability, combined with velocity scaling. Subjective user feedback further demonstrates a clear preference for DMP-based interactions. Objective evaluations, including physiological measurements from brain and skin activity, reinforce these findings, showcasing the advantages of DMPs in enhancing human-robot interaction and improving user experience.

This paper presents a Layered Behavior Tree (BT) architecture for improving decision-making and responsiveness in collaborative assembly for manufacturing value chains. The aim of the proposed software architecture is to ensure safety while maintaining productivity. To this aim, a multi-layered software architecture is proposed with three different layers, to handle different aspects of the context: the State Interpreters for context awareness, a Mode Handler for operational mode transitions, and the Executors for advanced behavior execution. By modularizing the logic and the mode handling of the application, and utilizing a shared memory for real-time communication between the layers, this architecture addresses the limitations of traditional monolithic BTs, achieving faster and more reliable responses to critical events while adapting the production to both the operator and context status. The proposed Layered Behavior Tree approach has been validated on an aerospace assembly use case, where a rough 79 % of reduction in the time to handle critical events has been appreciated in the measured results.

In the modern manufacturing industry, workers are still required to manually perform complex, repetitive, and physically demanding tasks. Collaborative robotics has emerged to assist human workers, reducing physical strain and monotony, while increasing productivity and safety. Nonetheless, cobots still struggle to match the dexterity of human hands and they lack the ability to understand natural language and interpret human needs, leading to worker frustration and stress. Psychological stress is a critical issue in industrial workplaces, as it affects both workers’ well-being and productivity. This work addresses the issue of operators’ well-being in the context of human-robot collaboration within industrial settings. We propose a multimodal approach to detect psychological stress during an industrial assembly task. Data are collected from 12 participants while performing both autonomous and robot-assisted assembly tasks. Our approach combines physiological signals (ECG, EMG, EDA), facial action units (AUs), and voice features to classify stress levels. The extracted features are combined using a late fusion approach involving the use of a self-attention layer. The results demonstrate the effectiveness of our model in predicting stress levels with a weighted F1-score of 0.81. This research paves the way for the development of more empathetic and human-aware robotic partners, capable of adapting their behavior to improve collaboration and operator well-being.

The accuracy of reconfigurable robot manipulators is a critical aspect which prevents their diffused industrial adoption. This work presents a novel model for designing joint motion profiles, particularly suitable for the motion planning of modular robots with high accuracy requirements. The model generates smooth motion profiles, without sacrificing execution time and taking into account the different characteristics of each joint and the requirements of each production task. The proposed method has been tested across the most performing motion planning approaches found in the literature, providing up to 39% faster jerk-bounded trajectories. Moreover, the model is flexible to the generation of adapted trajectories when degrading phenomena occur over the time.