This article investigates the problem of controlling the speed of robots in collaborative workcells for automated manufacturing. The solution is tailored to robotic cells for cooperative assembly of aircraft fuselage panels, where only structural elements are present and robots and humans can share the same workspace, but no physical contact is allowed, unless it happens at zero robot speed. The proposed approach addresses the problem of satisfying the minimal set of requirements of an industrial human–robot collaboration (HRC) task: precision and reliability of human detection and tracking in the shared workspace; correct robot task execution with minimum cycle time while assuring safety for human operators. These requirements are often conflicting with each other. The former does not only concern with safety only but also with the need of avoiding unnecessary robot stops or slowdowns in case of false-positive human detection. The latter, according to the current regulations, concerns with the need of computing the minimum protective separation distance between the human operator and the robots by adjusting their speed when dangerous situations happen. This article proposes a novel fuzzy inference approach to control robot speed enforcing safety while maximizing the level of productivity of the robot minimizing cycle time as well. The approach is supported by a sensor fusion algorithm that merges the images acquired from different depth sensors with those obtained from a thermal camera, by using a machine learning approach. The methodology is experimentally validated in two experiments: the first one at a lab-scale and the second one performed on a full-scale robotic workcell for cooperative assembly of aeronautical structural parts. Note to Practitioners —This article discusses a way to handle human safety specifications versus production requirements in collaborative robotized assembly systems. State-of-the-art (SoA) approaches cover only a few aspects of both human detection and robot speed scaling. The present research work proposes a complete pipeline that starts from a robust human tracking algorithm and scales the robot speed in real time. An innovative multimodal perception system composed of two depth cameras and a thermal camera monitors the collaborative workspace. The speed scaling algorithm is optimized to take on different human behaviors during less risky situations or more dangerous ones to guarantee both operator safety and minimum production time with the aim of better profitability and efficiency for collaborative workstations. The algorithm estimates the operator intention for real-time computation of the minimum protective distance according to the current safety regulations. The robot speed is smoothly changed for the psychological advantages of operators, both in the case of single and multiple workers. The result is a complete system, easily implementable on a standard industrial workcell.

Robots are becoming more widely used in the retail market, mostly for warehousing, but many in-store logistics processes are still difficult to automate. Both technological and methodological barriers hinder the use of robots for handling single products rather than the boxes containing them, as the experiences of the various editions of the Amazon Challenge demonstrated.

Aeronautical robotic applications use quite large, heavy robots with huge end effectors that are frequently multifunctional. An assembly jig to hold a fuselage panel and two medium-sized six-axis robots fixed on linear axes, referred to as the internal and the external robot with respect to the curvature of the panel, make up the Lean robotized AssemBly and cOntrol of composite aeRostructures (LABOR) work cell. A distributed software architecture is proposed in which individual modules are developed to execute specific subprocesses, each implementing innovative algorithms that solve the main drawbacks of state-of-the-art solutions. Real-time referencing adopts a point-cloud-based strategy to reconstruct and process the part before drilling, avoiding hole positioning errors. Accurate concentric countersink diameters are made possible through the automatic adjustment of the drilling tool with respect to the skin panel, which guarantees its orthogonality, as well as the implementation of process parameter optimization algorithms based on historical results that compensate for the wear of the drilling bits. Automatic sealing and fastening strategies that involve the measurement of the main fastener quality parameters allow for the complete verification of the entire assembly process of each part. Additionally, an advanced multimodal perception system continuously monitors the collaborative workspace to ensure safe human–robot collaboration (HRC) tasks. Through this integrated architecture, LABOR substantially reduces expenses and facilitates maintenance and programming.

In the context of Industry 5.0, human–robot collaboration (HRC) is increasingly crucial for enabling safe and efficient operations in shared industrial workspaces. This study aims to implement a hybrid robotic architecture based on the Speed and Separation Monitoring (SSM) collaborative scenario defined in ISO/TS 15066. The system calculates the minimum protective separation distance between the robot and the operators and slows down or stops the robot according to the risk assessment computed in real time. Compared to existing solutions, the approach prevents collisions and maximizes workcell production by reducing the robot speed only when the calculated safety index indicates an imminent risk of collision. The proposed distributed software architecture utilizes the ROS2 framework, integrating three modules: (1) a fast and reliable human tracking module based on the OptiTrack system that considerably reduces latency times or false positives, (2) an intention estimation (IE) module, employing a linear Kalman filter (LKF) to predict the operator’s next position and velocity, thus considering the current scenario and not the worst case, and (3) a robot control module that computes the protective separation distance and assesses the safety index by measuring the Euclidean distance between operators and the robot. This module dynamically adjusts robot speed to maintain safety while minimizing unnecessary slowdowns, ensuring the efficiency of collaborative tasks. Experimental results demonstrate that the proposed system effectively balances safety and speed, optimizing overall performance in human–robot collaborative industrial environments, with significant improvements in productivity and reduced risk of accidents.

In Industry 5.0, the transition from fixed traditional automation to flexible human–robot collaboration (HRC) needs interfaces that are both intuitive and efficient. This paper introduces a novel, multimodal control system for autonomous object handling, specifically designed to enhance natural user interaction in dynamic work environments. The system integrates a 6-Degrees of Freedom (DoF) collaborative robot (UR5e) with a hand-eye RGB-D vision system to achieve robust autonomy. The core technical contribution lies in a vision pipeline utilizing deep learning for object detection and point cloud processing for accurate 6D pose estimation, enabling advanced tasks such as human-aware object handover directly onto the operator’s hand. Crucially, an Automatic Speech Recognition (ASR) is incorporated, providing a Natural Language Understanding (NLU) layer that allows operators to issue real-time commands for task modification, error correction and object selection. Experimental results demonstrate that this multimodal approach offers a streamlined workflow aiming to improve operational flexibility compared to traditional HMIs, while enhancing the perceived naturalness of the collaborative task. The system establishes a framework for highly responsive and intuitive human–robot workspaces, advancing the state of the art in natural interaction for collaborative object manipulation.

This work proposes the application of several smart strategies for object manipulation tasks. A real-time flexible motion planning method was developed to be adapted to typical in-store logistics scenarios. The solution combines and optimizes some state-of-the-art techniques to solve object recognition and localization problems with a new hybrid pipeline. The algorithm guarantees good robustness and accuracy for object detection through depth images. A standard planner plans collision-free trajectories throughout the whole task while a proposed reactive motion control is active. Distributed proximity sensors were adopted to locally modify the planned trajectory when unexpected or misplaced obstacles intervene in the scene. To implement a robust grasping phase, a novel slipping control algorithm was used. It dynamically computes the grasp force by adapting it to the actual object physical properties so as to prevent slipping. Experimental results carried out in a typical supermarket scenario demonstrate the effectiveness of the presented methods.