Veronica Campana | IDRA Labs

MAGICIAN

Sun, 01 Oct 2023 00:00:00 +0000

This is a research and innovation project funded by Horizon Europe. Website: https://magician-project.eu/.

4-year EU project: October 2023-September 2027
11 consortium partners from 7 countries across Europe coordinated by Università di Trento
1 Automotive Use Case and extension of Use Cases through 2 Open Calls

Vision

MAGICIAN will develop robotic solutions to classify and rework defects from semi-finished products autonomously before the finalization of product aesthetics.

These solutions are designed to be modular, applicable to various manufacturing fields, reducing physical strain and enhancing safety for human operators.

Challenges

Consumers increasingly expect manufacturing products to be free of defects which sets high standards for the production process. However, associated working processes are physically and cognitively demanding for workers and executed in a potentially hazardous environment.

Solutions

Two modular robotic solutions:

a sensing robot for defect analysis (SR)
a cleaning robot for reworking defects (CR)

Both robots will use AI modules to perform associated operations. Data needed for these AI modules will be gathered by learning from workers operating on semi-finished products.

Human-centred approach: MAGICIAN applies a human-centred design strategy to shape the progress of automation and human-robot collaboration in manufacturing towards an emphasis on trust, empathy, and ethics.

Use Cases: MAGICIAN solutions will be tested in an automotive manufacturing use case. Both robots will be coupled with human operators during the testing to ensure trust-based human-robot collaboration. Additional use cases will be engaged through two Open Calls.

Impact

Innovative robotic components for mechanical working operations allowing for human-robot collaboration;
Improved productivity in manufacturing and maintenance;
Improved health and safety conditions for human workers and focus on added value operations;
Tested applicability of solutions for various manufacturing application fields;
Strengthened trust in AI and robotic technologies.

Veronica Campana

Sun, 01 Oct 2023 00:00:00 +0000

Veronica Campana is a PhD student in Materials, Mechatronics and Systems Engineering at the University of Trento. Her research focuses on developing estimation and learning algorithms for safe motion control of robots in workspaces shared by robots and humans.

Intelligent Manufacturing Robotics

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Robotics involves the design and deployment of automated machines that can perform complex tasks such as assembly and disassembly, polishing and quality inspection with minimal human intervention. AI enhances these systems by equipping them with the ability to analyse data, learn from patterns and human behaviours, and make decisions in real-time, enabling smarter, healthier and more flexible production processes. Together, the IDRA robotics and AI solutions contribute to advancements like human aware motion planning, imitation and transfer learning, flexible localisation and estimation and activity scheduling and motion planning, driving innovation in modern manufacturing.

Human Aware Motion Planning

Human-aware motion planning is a critical aspect of modern manufacturing, where robots (or cobots) and humans increasingly collaborate in shared workspaces. This approach ensures that robotic systems can operate efficiently while prioritising human safety and comfort. Also, this approach not only enhances safety but also improves productivity by enabling smoother and more intuitive collaboration between humans and robots, paving the way for more adaptive and human-centric manufacturing environments. Our research aims to:

Human motion models leveraging sensors, machine learning, and predictive algorithms.
Stochastic predictions and classification of human motions.
Safe reactive motion planning, reactive control and safe reinforcement learning.

Flexible localisation and estimation

In modern manufacturing, localisation of Automated Guided Vehicles (AGVs) is vital for efficient navigation and material handling. Accurate position tracking ensures seamless movement and reduces operational delays. Robot state estimation complements this by providing real-time data on the robot’s position, orientation, and operational state, enabling precise task execution. Ergodic control optimises robot motion by ensuring uniform exploration of a workspace, balancing efficiency and thoroughness in tasks like inspection or material distribution. Together, these technologies enhance manufacturing automation, improve workflow efficiency, and minimise errors. Key features of our research include:

Advanced sensor fusion techniques and machine learning-based models for robust robot localisation in dynamic manufacturing environments.
Adaptive ergodic control strategies for multi-robot systems and active sensing in large-scale manufacturing facilities.
Autonomous decision-making systems for dynamic task allocation based on robot state estimation.

The Team

Matteo Saveriano - Professor
Marco Roveri - Professor
Songchun Gao - Researcher
Matteo Dalle Vedove - PhD Student
Elena Basei - PhD Student
Veronica Campana - PhD Student
Edoardo Lamon - Collaborator

Publications and Events

For detailed information on our research and publications, visit our publications page.

Contact Us

For more information about our research or to collaborate with us, please contact songchun.gao@unitn.it.