ICON – agIle eleCtric mOtor maNufacturing

Name of demonstration

ICON - agIle eleCtric mOtor maNufacturing

Main objective

The main objective of the demonstrator is to make the manufacturing process of electric motors more agile, by deploying three TRINITY-originated modules in a production line. This leads to: increased HRC level and productivity in the coils winding step, waste material saving, economic and social benefits.

Short description

This demonstrator introduces three TRINITY-originated modules into an existing robotic cell for coils winding. Thanks to them, the cell is enhanced with new collaborative skills. The “Safe Human Detection in a Collaborative Work Cell” module allows the operator to work safely alongside the robot, creating dynamic and adaptive safety areas. Thanks to the “Projection-based Interaction Interface for HRC” module it is possible to control the robot using an innovative interface projected on the working-table. The “Object Classification” module allows to automatically recognize different stator designs and load the related parameter set to generate the robot trajectory to wind the coils.


This demonstration has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825196.

Owner of the demonstrator

Stam S.r.l.

Responsible person

STAM Business Area Manager – Robotics and Mechatronics, Stefano Ellero


ICPE Servomotors Department Manager, Paul Minciunescu



Robotics, human-robot collaboration, industrial robotics, safety, object classification.

Benefits for the users

Thanks to this demonstrator, the manufacturing process of electric motors is:

        More agile: reduced time for cell reconfiguration.

        More productive: less time wasted in cell reprogramming and task parallelization.

        More automated and with an increased level of HRC.

In particular, it is possible to achieve:

        Time reduction: for the HRC-based process, which allows the operator performing preparatory and finishing tasks on a stator while the robot is winding another stator.

        Increased operators’ satisfaction and trust: following the deployment of HRC tasks.

        Material cost saving: thanks to the HRC-based winding process instead of semi-automatic process.

        CO2 emissions saving: thanks to material saving.


The low flexibility of automated winding machines and conventional robotic systems, i.e. the time and costs required to switch from one design to another, coupled to their high cost, force small manufacturers to employ human operators in this task, who are obviously much more flexible, but more expensive (because of labour cost and equipment). Moreover, if the robotic cell responsible for coils winding is not able to guarantee the safety of the operator, no collaboration can be achieved between the human and the robot and therefore, while the robot is active, the operator has to stay behind a safety fence. Thanks to the three TRINITY-originated modules deployed in this use-case demonstrator, the implementation of collaborative tasks is possible, achieving multiple benefits. It is possible for the operator to work safely alongside the robot, significantly reducing idle times and speeding up the process.

Risks and limitations

ROS compatibility: this use-case demonstrator is based on ROS. The system has been tested with ROS supported hardware, such as Intel RealSense and Microsoft Kinect RGB-D cameras, the compatibility with different hardware depends on the ROS driver availability of the intended hardware. Knowledge of ROS: developers need to be familiar with ROS and its communication protocols. Cybersecurity aspects: since for the object classification task a dataset of stator pictures has to be acquired, further consideration on cybersecurity should be done to protect data from intellectual property theft, such as theft of proprietary design or pictures.

Technology readiness level

6 - Safety approved sensors and systems are commercially available

Sectors of application


Potential sectors of application

Manufacturing: various sizes companies dealing with manufacturing systems that aim to increase automation in their processes or companies that already have robotic cells in their production line and want to introduce collaboration between the operator and the robot. Thanks to this technology, companies would be able to improve the safety level of the robotic environment and make the robot perform the most repetitive and heavy tasks.

Patents / Licenses / Copyrights
Hardware / Software


KUKA KR6 sixx collaborative robotic arm.

KRC4 compact robot controller.

Rotary table combined with a self-centering chuck.

Industrial PLC: for handling the communication between the various components.

Control panel: to manage the basic signals for activation, start-up and emergency stop of the robotic cell.

End-effector: for winding the wire.

Linux industrial PC running ROS: for the software computations.

Lidar sensor: for detecting the proximity of the operator to the robot (such as KEYENCE SZ-01S).

Projector: for projecting the user interface components and the safety contour on the workspace (such as SHARP XR-32s).

RGB-D camera: for monitoring the depth values of the workspace and thus detecting the interaction of the operator with the interface (such as Microsoft Kinect v2).

RGB-D camera: for stator classification (such as INTEL RealSense D415).


TRINITY module: Safe Human Detection in a Collaborative Work Cell

TRINITY module: Projection-based Interaction Interface for HRC

TRINITY module: Object Classification



ICON - Module 3 - The ML algorithm classifies the stator and automatically inputs the related manufacturing parameters to generate winding trajectories
ICON - Module 1 - Operator's safety is ensured by the sensor tracking him/her and reducing robot speed accordingly
ICON - Module 2 - The interface is projected on the working table, as well as the robot contour; in case of violation of the contour (e.g. by the operator's hand), the robot stops

Promotional video of the ICON project


To learn more about the solution, click on the link below to access the training on the Moodle platform
Agile Electric Motor Manufacturing

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