Collaborative robots (cobots) have emerged as a pivotal paradigm for the upcoming leap to Industry 5.0. In recent years, the range of applications has expanded significantly, particularly in assembly tasks within the manufacturing industry. The primary goal of this paper is to review the application of cobots in industrial assembly tasks, highlighting possibilities for innovative research in smart robotics, including prospects for challenging applications in aircraft final assembly processes. The paper systematically reviews recent literature to analyse the use of collaborative robotics in industrial assembly tasks, encompassing characterisation of application environments, motivations, characteristics and outcomes of relevant use cases across various industrial segments. Additionally, it reviews a set of innovative technological patents issued by the aeronautical industry over the past 14 years, highlighting trending projects in industry. The investigation reveals that the automotive and electronics industries remain at the forefront of cobot applications, mainly for tasks like pick-and-place operations and component manipulation. Applications in open work cells, where humans and robots operate at supportive or sequential interaction levels, using conventional communication interfaces and camera-assisted technologies, have been the most prevalent. The review identifies potential opportunities and key aspects from future application scenarios for cobots. The findings are relevant to the industrial robotics community, emphasising the need for novel applied research on human–robot colla boration in aeronautical industry.

The functionality and aesthetic of 3D-printed components can be compromised if visible defects appear on their external surfaces. To overcome this issue, CNC machines were traditionally adopted for milling machining. More recently, industrial robots have been demonstrated to be a valid alternative. This study presents a robotic workstation developed for contouring machining 3D thermoplastic components printed using the material extrusion technology. The workstation adopts a collaborative robot with a novel, custom-designed, and low-cost end-effector made of a powered contouring tool integrated with three load cells for measuring the cutting forces along three perpendicular directions. The tool path planning is defined by a proposed and validated procedure. By a vision algorithm and a touch-stop operation, the 3D CAD model-based tool path is adapted to the current position and orientation of the workpiece. The experimental activity for determining the optimal set of contouring machining parameters (rotational speed, cut depth, and feed rate) and for measuring cutting forces confirms the feasibility of adopting the cobot-based solution for this application and suggests potential improvements for future works.

Collaborative robotics is a field of growing industrial interest, within which understanding the energetic behavior of manipulators is essential. In this work, we present the electro-mechanical modeling of the UR5 e-series robot through the identification of its dynamics and electrical parameters. By means of the identified robot model, it is then possible to compute and optimize the energy consumption of the robot during prescribed trajectories. The proposed model is derived from data acquired from the robot controller during bespoke experimental tests, using model identification procedures and datasheet provided by manipulator, motors, and gearbox manufacturers. The entire procedure does not require the use of any additional sensor, so it can be easily replicated with an off-the-shelf manipulator, and applied to other robots of the same family.

This paper explores and experimentally compares the effectiveness of robot-stopping approaches based on the speed and separation monitoring for improving fluency in collaborative robotics. In the compared approaches, a supervisory controller checks the distance between the bounding volumes enclosing human operator and robot and prevents potential collisions by determining the robot’s stop time and triggering a stop trajectory if necessary. The methods are tested on a Franka Emika robot with 7 degrees of freedom, involving 27 volunteer participants, who are asked to walk along assigned paths to cyclically intrude the robot workspace, while the manipulator is working. The experimental results show that scaling online the dynamic safety zones is beneficial for improving fluency of human-robot collaboration, showing significant statistical differences with respect to alternative approaches.