Manufacturing applications increasingly integrate visually aided robotic systems. Such systems must rely on excellent kinematic parameter calibration and a hand – eye matrix estimation to perform according to standards. The latter is as precise as the camera pose estimation capability and the robotic forward kinematic precision. To enhance the overall system’s precision, one must simultaneously act and improve the robot’s kinematic parameters and hand – eye transformation due to mutual inference. This work exploits standard 2D camera systems to simultaneously estimate the kinematic parameters and the hand – eye transformation matrix through a method based on the Unscented Kalman Filter (UKF) and the parameters uncertainty transportation through the robot’s kinematic. The method employs data gathered during the robot movements and camera readings and iteratively improves the system parameters’ estimate. The method is applied to industrial mobile manipulators and tested on both synthetic data and real experiment data, showing a great improvement in the kinematic parameters estimation.

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.

UMA-2 is a wheeled mobile platform equipped with a vacuum adhesion system, eight actuated joints and four passive ones, designed to climb vertical and curved surfaces. The platform can perform maintenance tasks such as corrosion removal and cleaning with grinding while climbing. The quality of the repairing process is largely affected by grinding process parameters including tool forces, toolpath and the robot trajectory accuracy. The current work introduces a trajectory analysis and adaptation model to control the UMA-2 platform to ensure specific surface quality KPIs and incorporating the effects of robot compliancy. The proposed trajectory analysis has been extensively validated through experimental campaigns representative of maintenance in wind power industry.

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.

Highly automated production systems are conceived to efficiently handle evolving production requirements. This concerns any level of the system from the configuration and control to the management of production. The proposed work deals with the development of an innovative platform jointly managing the production scheduling level and the automation level. The major advantage coming from the platform is the capacity of generating scheduling plans which are executed at automation level and concurrently monitored over time so that any production anomaly or system misbehavior can be dynamically interpreted and adapted by regenerating online a new schedule. The paper describes the current release of our closed loop architecture that integrated both control and automation parts.

Highly reconfigurable and agile production systems are selected to operate in production contexts often characterized by changes of the production requirements or changes of the part family demand. The operational level for such system architecture is expected to manage the short term production planning while guaranteeing the automation layer enables physical devices to exploit logic control tasks within the specific time buckets. The proposed work outlines an integrated approach supporting the operational level for RMSs in which the scheduling of production jobs and the scheduling of corresponding automation tasks are dynamically coupled. Connections with consolidated constraint-based representation and solving techniques are also discussed. The integrated scheduling approach has been validated with reference to a Finishing Robotic Cell (FRC) operating in a pilot assembly line.