trinity

Objective

- The main objective of this project is to showcase advanced robotics solutions that increase the agility of the automotive cable production industry.

useCases

Use Cases

Three use-cases are exploited:

- Collaborative robotics, demonstrate a collaborative assembly of different car fuse boxes between a human and a robot. Safety areas and a safety line are projected on the assembly station to keep the worker aware of his working space;

- Human-Robot Interface (HRI) using an Augmented Reality (AR) application for training-on-the-job and assisting workers in maintenance operations;

- Bin-picking with machine vision where a robot detects different types of connectors in unpredictable positions, determines the best grabbing position and sort them in different output trays, depending on the connector type.

Partners
Partners


Allbesmart, a Portuguese SME that provides R&D services on ICT for several industries, specialized in communication networks, IoT solutions, AI and AR applications.
This demonstrator includes collaboration with StoneShield (www.stshield.com), a robotic integrator that is the adopter of the developed technology.
StoneShield has a solid understanding of the automotive cable assembly industry, its requirements, and main challenges in its digital transformation. 

Results

- Collaborative assembly reduction of 30%: Collaborative assembly has the potential to considerably reduce assembly times, whilst ensuring, reduction in human errors by inspecting the final product quality. They may also increase workplace ergonomics. Using a mid-range collaborative robot, assembly times can be reduced by, at least, 30%.  

- Training-time reduction by 50%: Augmented Reality (AR) based solutions improve knowledge transfer, and reduced training time by 50%. They also improve field service quality and productivity, reducing overtime spend with maintenance operations.  

- Cost savings up to 70%: AR with remote video assistance considerably reduces, the need for travels related to maintenance.  

- Automated bin-picking with cycle times below 10s: AI machine vision is currently an accessible technology that can be coupled with affordable robotic machines to perform automated tasks of bin-picking with ordered sort, which are applicable in many industry use cases. Mid-range robots can achieve cycle times below 10s, depending on the type of target objects and the robot gripper (clamp or suction).  

Video demonstrator

Click to see the video

trinity
Application

Manufacturers of robotic machines:
- Machine vision algorithms to assist automated tasks and perform quality control (visual inspection) of assembled products.
- Augmented reality applications for training-on-the-job and assistance in machine maintenance operations.

Industries with manual assembly processes:
- Collaborative robots (cobots) assisted by machine vision and safety systems, for a quicker, more efficient assembly process, also ensuring quality in assembled products by reducing worker fatigue.

Maintenance companies:
- Augmented reality solutions to support technicians in the maintenance operations for many different types of machines (e.g., heating, ventilation and air conditioning (HVAC)).

Contact us

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

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