Augmenting Human Operators for the Era of Automated Industry - A/HOPE/AI
Reference number | |
Coordinator | KUNGLIGA TEKNISKA HÖGSKOLAN - School of Electrical Engineering and Computer Science |
Funding from Vinnova | SEK 490 000 |
Project duration | November 2018 - August 2019 |
Status | Completed |
Venture | The strategic innovation programme for Production2030 |
Call | Idea projects for new services for manufacturing industry |
Important results from the project
The project established a consortium exploring innovative solutions to support human operators in performing industrial tasks, considering low latency 5G networks and upcoming Industry 4.0 applications. Based on input from project partners and their immediate industrial ecosystem, a number of relevant applications of AR/MR have been identified and developed in an experimental testbed. These have been tested with real users to quantify the impact of the proposed solutions on both the quality of work life and efficiency in accomplishing critical tasks.
Expected long term effects
- Examples of novel digital-to-physical interaction modalities that are specific for human-centric industrial applications. - A set of demonstrators showcasing (a) Human-to-Human (b) Human-to-Machine and (c) Human-to-AI solutions for Industry 4.0 tasks. - Initial experimental results and data from user testing. - The establishment of an advanced “sandbox” for building and testing novel VR / AR based industry 4.0 services.
Approach and implementation
A core component of the project activities consisted of workshops designed to identify key areas of applications that are unfulfilled by existing technologies. Based on that, an initial set of demonstrators have been developed and used in workshops with external industrial partners to 1) refine the concepts based on their expertise and experienced pain points, 2) to engage them in project ecosystem for designing a joint follow-up research project. The refined demonstrators have then been used with real users to quantify KPIs aiming at both QoE assessment and production efficiency.