Our Services

Below is just an extract of some of the services available in our catalogue. There are a lot more. Please contact us for more information.

AR & VR for operator support & training
Node:

This service enables the experimentation with AI in the context of safety management and flexible prorduction systems with the use of Augmented Reality (AR)/Virtual Reality (VR) tools for Human robot collaboration. AI empowered algorithms for

Synthetic data for training machine learning models
Node:

Experimentation with the concept of training machine vision solutions with the use of synthetic training data. A set of reference parts will be used to train your machine vision solution. The result of the service

Layout planning optimization 
Node:

This service is offered for the testing and experimentation on AI-enabled algorithms for layout optimization. The capabilities and potential of virtual reality simulation for intuitive and multi-stakeholder engagement can be exploited for the comprehensive and

Industrial IoT platform, analytics & visualization
Node:

This service provides an industrial testbed for research and experimentation. With the aid of this service, engineers and researchers can set up Internet of Things sensors, gather and process data instantly, and view operational metrics

Dynamic robot task planning & resources orchestration
Node:

Dynamic Robot Task Planning & Resources Orchestration service focuses on optimizing robot task execution in dynamic environments. Within this service it is possible to experiment with the potential of AI to enable robots to adapt

Intuitive robot programming
Node:

This service is dedicated to the testing and experimenetation with AI tools enabling intuitive and ad hoc robot programming. A diverse set of collaborative robotic cells, including low payload, high payload, and mobile robots, offer

Industrial Digital Twins
Node:
report describing the benefits or disadvantages of implementing the investigated scenario or report discussing the performance of the tested AI solution or demonstration of tested solution and report describing the benefits of the solution
Consultancy in metrology topics relevant for manufacturing
Node:
Demonstrate benefits of the Digital Metrological Twin for product quality control
Certification of 3D coordinate data evaluation algorithms
Node:
Developers of 3D coordinate data evaluation algorithms can document level of agreement of their data fits with reference data
ROS2 conformance check
Node:
Report with Test Results and Evaluation
AI-based control of electric machines
Node:
Feasibility report, measurement results
Test of digital twin for product traceability
Node:
Category:
Demonstrate benefits of the Digital Twin technology for product traceability
Identify relevant Tests, Experiments, and key partners towards enabling or prooving maturity of AI solutions for integration and deployment
Node:
The service must enable the company to initiate further test or experimentation services
Identify relevant Tests, Experiments, and key partners towards enabling or prooving maturity of AI solutions for integration and deployment
Node:
The service must enable the company to initiate further test or experimentation services
Identify relevant Tests, Experiments, and key partners towards enabling or prooving maturity of AI solutions for integration and deployment
Node:
The service must enable the company to initiate further test or experimentation services
Identify relevant Tests, Experiments, and key partners towards enabling or prooving maturity of AI solutions for integration and deployment
Node:
Category:
The service must enable the company to initiate further test or experimentation services
Use the digital twin of an operator to improve workstation ergonomics
Node:
Provide recommandations to optimize the workstation ergonomics
Use of simulation to optimize production lines
Node:
1) optimize production lines reconfiguration by virtual commissionning 2) optimize use and maintenance of an industrial equipement (robot cell, production machine) with the digital twin updated via the cloud, based on data collected during product
Use of simulation for process planning and monitoring
Node:
1) optimize the conception of production lines, increase availabilty of production lines, optimize the orchestration of tasks
Testing vision based geolocalisation technologies in specific industrial context
Node:
Verify that the technology meets the requirements in manufacturing context
Testing tools for 3D scene analysis and human detection
Node:
Identify best-fit algorithms
Testing of Virtual Reality (VR) technology to learn technical gesture
Node:
Demonstrate the benefits of VR technology to learn gesture and apply it to robotic systems
Testing Natural Language Processing
Node:
Verify that the technology meets the requirements in manufacturing context
Experiment AMR in specific environment
Node:
Evaluate mobile robot capacities on industrial use-cases
Testing of intelligent robot calibration methods
Node:
Demonstrate the benefit of the technology
Test of intelligent grasping – object recognition and manipulation
Node:
Demonstrate capabilities of intelligent grapsing technology on specified use cases
Setting up test-bed for automatic disassembly task
Node:
Category:
Assess the performance of the system (speed, accuracy, efficiency)
Give acces to an environnement that enables SMEs to test and experiment their own AI-based solutions
Node:
Supporting SMEs in the test of AI based solutions for manufacturing applications
Experiment connectivity solutions (private 5G, Ethernet TSN) for optimized use in the factory
Node:
Guide the choice of network infrastructure before investment Demonstrate achievable performances
Model Based Systems Engineering (MBSE) services to specify and design complex industrial systems.
Node:
Supporting SMEs in the design of complex systems for manufacturing applications
Use of cobotic system for operator assistance in effort
Node:
Demonstrate the capabilities of a cobotic system to assist an operator in effort
Setting up a prototype to test manufacturing tasks with a cobot
Node:
Evaluate the performance of cobots in industrial context (material, geometry of product, …)
Experiment teleoperation systems
Node:
Evaluate the performance of teleoperation system in industrial context
Use of AI algorithm for autonomous decision making
Node:
Enable a system or a robot to make a decision and act in a given situation
Readiness and reliability of AI based solutions in manufacturing context
Node:
Increase SMEs confidence in AI-based solutions
Awareness making through sharing of expertise, knowledge and demonstrations of AI based technologies
Node:
Category:
Convincing a company to go further in test and experimentaion of an AI technology
Position measurement and tracking system
Node:

High-frequency and high-accuracy measurements can help to understand flexibibity/modeshapes of flexible products, or help to understand dynamic behaviour of products in a pile or on a moving conveyor belt.

Robot learning by demonstration
Node:

To minimize time spent in programming how to perform a task, the user can teach the robot by demonstration. This can be done by first stearing the robot by hand, while monitoring its state. Eventually

Single and dual arm cobot testbed
Node:

Experiment with one or two Panda cobots to investigate if they can add value in the production line. A single arm could be utilized for product singulation, e.g. in a bin- or heap picking task.

Testbeds for mobility tasks
Node:

Experiment with mobile platforms that might be used in a manufacturing hall to get products from point A to B across the shop floor.

Technology providers: Test with exchanging manufacturing information across the value chain
Node:
Technology evaluation, demonstratror
Manufacturing companies: Experiment with exchanging manufacturing information across the value chain to enable the transition towards remanufacturing
Node:
Technology evaluation, demonstratror
Experimenting with human robot interaction
Node:
Demonstrator
Experimenting with & deploying digital operator support technology to shorten learning time and increase employability & quality
Node:
Technology evaluation
A proof of concept of how to deploy the Digital Product Passport across the value chain
Node:
Category:
Technology evaluation, demonstratror
Selecting Digital & AR-MR operator support technologies
Node:
Technology evaluation
AI Factory Planner
Node:
Demonstrate that application of AI reinforcement learning can greatly improve efficiently and reduce costs as compared to conventional heuristic production planning
Increase speed, decrease cost, seize control of high mix, small series manufacturing characterized by infinite shape & material variation by end-to-end integration, digitalization, and factory and operator intelligence opportunities
Node:
Evaluation of ability to leverage existing technology for high variability of parts
Sorting of parts in order to reduce complexity, improve control of high mix, small series manufacturing characterized by infinite shape & material variation
Node:
- Evaluation of ability to leverage existing technology modules to cope a higher variety of parts beyond what is currently possible. - The gained knowledge and infrastructure applied to new segments of applications & SMEs