|Funding Organization:||General Secretariat for Research and Technology|
|Funding Programme:||Ειδικές Δράσεις “Υδατοκαλλιέργειες” - “Βιομηχανικά Υλικά” - “Ανοιχτή Καινοτομία στον Πολιτισμό”|
|Funding Instrument:||Business Partnerships with Research Organizations|
October 11, 2019
|Total Budget:||599,554.84 EUR|
|ITI Budget:||200,700.00 EUR|
Nowadays, there is a growing demand for personalized products of great differentiation, while the production lines must respond immediately, without the level of customer service and the quality of the final product being affected. The aim of the Q-CONPASS project is to develop an innovative intelligent system to monitor the production process of highly differentiated products based on the use of autonomous robotic vehicles to optimize quality control and ensure continuous improvement of processes in industrial areas. The integrated system will use low-cost vehicles equipped with suitable sensors, such as RGB-D cameras. The general objectives of the project are: (a) To ensure the quality of the final product on the production line and (b) to monitor ergonomic characteristics of the workers, as well as identify sources of risk regarding health and safety in the workspace.
The technological goals of the project that will lead to the achievement of the aforementioned general objectives are: 1. The design of a flexible, customizable and expandable platform that will be used in production lines with different topologies and characteristics and monitor and optimize the quality control process. 2. The development of robotic navigation and mapping methods that will be integrated in the robotic vehicles of the system, allowing their autonomous and safe navigation in the industrial environment. 3. The development of methods that control in real-time the robotic vehicles of the system, as well as the design of user friendly interfaces for interaction. 4. The development of methods for the identification and tracking of objects and human movements in order to ensure the correct implementation of the quality control process, as well as assess ergonomic factors and risks. 5. The statistical modeling of parameters of the production process and on this basis, the development of a decision support system for the production manager. 6. The demonstration and validation of the system using two pilots, which will be integrated into the actual quality control process in an industrial environment.