Approach
COGNIMAN aims to create a flexible and efficient manufacturing process that produces zero waste and high-quality products. We begin by collecting and analysing the conditions of selected manufacturing scenarios, addressing legal, procedural, ethical and technical challenges. This helps us identify the key components for an AI-enhanced robot system in a flexible smart manufacturing setup.
Our approach also focuses on human-centric technological design. We develop capabilities like Digital Twin simulations, advanced sensing, machine learning and cognitive robots for each pilot. These components are then integrated and pre-tested in a representative environment before undergoing extensive trials at the manufacturing plant.
Cognitive Digital Twin and simulations
We plan to expand the COGNITWIN Toolbox for deploying DTs in manufacturing and robotics. The pipeline includes sensing, knowledge, reasoning, and action steps. The Toolbox manages data, provides semantic maps, and uses simulation libraries based on Robot Operating System (ROS).
Advanced sensing
We aim to create a multimodal sensing system for cognitive manufacturing and robotics, with sensor suites designed for the use cases. The glass fibre pilot will use cameras, lasers and other sensors to gather detailed information. The precision machining and additive manufacturing pilot will use advanced cameras and light-based sensors. The steel manufacturing pilot will focus on sensors for safety, identifying products and storage and helping drones navigate safely in harsh indoor conditions.
Machine learning for manufacturing
We aim to create a machine-learning toolbox for tasks like fibre break detection, classification and safety prediction. It combines deep neural networks and graphical models for efficient sensor analysis. The project focuses on handling large datasets and enabling on-board AI for low-power computing. Challenges include real-time interpretation of portable hardware. Solutions include reduced network precision, smaller networks and new convolution approaches.
Robotics in manufacturing
Robot guidance for deburring/additive manufacturing: Optimal path planning for deburring/additive manufacturing using 3D maps and vision-based navigation. Continuous learning and safety measures ensure high-quality workmanship.
Drone for autonomous detection in metal production: Multi-drone solution for steel production warehousing, optimising logistics through RFID-based detection and active area mapping.
Collaborative robotics: Safe and efficient collaboration between robots, humans and machines. Long-term SLAM system and shared-awareness framework enhance performance.
Human centric design
COGNIMAN aims for human-centric smart manufacturing by exploring human-machine-robot teaming and developing guidance procedures. These key research questions will be investigated: optimal human-AI relationship and principles of interaction and achieving explainable, trustworthy and ethical AI. The project utilises human-in-the-loop AI learning for improved training and system output.