Collaborative Robotics In Large Scale Assembly, Material Handling And Processing

Name of demonstration

Collaborative Robotics In Large Scale Assembly, Material Handling And Processing

Main objective

Main objective of this use case is to demonstrate the possibilities of large-scale industrial robotics in collaborative tasks. This use case demonstrates a novel combination of safety sensors and additional devices that make true human-robot collaboration possible, while still following safety regulations and standards. In addition, dynamic and flexible robot trajectory generations are demonstrated.

Short description

An agile industrial robotization of a large-scale material handling, processing or prefabrication where robots and people will process components collaboratively is demonstrated. The working zone is monitored dynamically and information is provided to both parties: the human worker and robot. Different multimodal human-computer interaction methods are evaluated. This ultimately leads to more agile robotized production, where humans and robots may work together in tasks such as large-scale assembly, material handling and processing

Owner of the demonstrator

Centria University of Applied Sciences

Responsible person

Senior Research Scientist, Sakari Pieskä

tel.: +358 44 449 2564


C33.2 - Installation of industrial machinery and equipment


Industrial robot, Collaborative robot, Robot safety, Robot trajectories.

Potential users

Robot and automation integrators: companies that program and deploy industrial robots for automated manufacturing tasks can use the demonstrated systems in realised robot cells. Companies dealing with large-scale materials: companies carrying out large-scale material handling, processing or component pre-fabrication. Typically the robot integration is bought as a service from the aforementioned operators.

Benefits for the users

Enhanced and dynamic safety: more flexible production and operations related to robotics.

Human-robot-collaboration: HRC using industrial size robots.

Saved space: smaller factory footprint and therefore lowered costs due to fenceless robot cells.


Conventionally, industrial robots have been isolated by fences to prevent accidents and as a result, tasks involving large-scale workpieces and human interaction have been challenging to implement into robotized production. While still following regulations and standards, it is possible to implement safe fenceless industrial robot cells that allow a true human-robot collaboration. This demonstration is a novel combination of multiple safety-related sensors of which some are safety-approved and others are additional sensors increasing agility, flexibility and safety of the manufacturing process.

Risks and limitations

Lack of incompatible specific software needed in integration may be a limitation, especially for aged industrial robots. Every robot needs a safety software (e.g. ABB SafeMove, KUKASafety) to assure safe and reliable operation. For aged industrial robots the modern software is not necessarily available.

Technology readiness level

6 - Safety approved sensors and systems are commercially available

Sectors of application

Construction, Mechanical engineering.

Potential sectors of application

Maritime industry and Automotive industry

Patents / Licenses / Copyrights

Individual HW components are commercial off-the-shelf products.

Usage of modules is free excluding AutoMAPPPS, which is property of Convergent IT.

CloudCompare is released under GPL.

Other software developed at Centria is released under MIT license.

Hardware / Software


Safety laser scanner: e.g. Pilz PSENscan or Sick S300 safety scanner for spatial robot cell safeguarding.

Microwave radar: e.g. Sick SafeRS microwave radar that operates in challenging industrial environment containing dust, smoke, darkness, steam etc. for person detection.

Indoor positioning system: additional safety system of RF tracking or other precise indoor positioning system such as Quuppa Intelligent Locating System.

360 camera: e.g. 360Fly as an additional safety system that together with artificial intelligence can be used for person detection.

3D point cloud creation hardware: 3Dcamera: e.g. 3D Kinect, Intel RealSense, Orbec or LIDAR or 3D Scanner e.g. PhotoXi or Artec Leo 3D Scanner. Used for robot trajectory generation


ROS and/or ROS-Industrial: dynamic trajectory programming

AUTOMAPPPS: reactive (online) robot trajectory programming

CloudCompare: 3D-point cloud processing

RoboDK: dynamic trajectory programming, robot programming in general, and simulation

CDS Config and diagnostic software: configuration and diagnostic software for SICK safety products

SICK safeRS Designer: configuring and diagnostics of safe radar sensor(s)

Quuppa Positioning Engine (QPE) or similar for indoor positioning: used to calculate tracking tag locations based on information tags are transmitting to the server

Custom software for other additional sensors: accessing and analysing data from 3D cameras, 360 cameras, LIDARS and 3D scanners. machine learning framework such as PyTorch


Online training material will be available through the TRINITY training platform that contains separate training material for use cases and modules.


To learn more about the solution, click on the link below to access the training on the Moodle platform
Collaborative robotics in large scale assembly, material handling and processing

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