RoboMAG
What
AI implementation and development of a
dashboard for robot utilisation and monitoring.
Year
2023/2024
Category
AI, Innovation
Technology
AI, Robotics, Aggregate Programming
Cliente
To realize your project
How it all started
With the support of CTE NEXT, the RoboMAG project by Synesthesia (funded by CTE-NEXT) was developed as part of the Call4Testing initiative. In collaboration with the Computer Science Department of the University of Turin and the Fondazione Links, Synesthesia was able to carry out the experimentation with the valuable contribution and availability of Leroy Merlin.
RoboMAG is an innovative and sustainable solution utilising autonomous robots to enhance logistics and delivery management in urban areas. The robots can recognise products on shelves and autonomously coordinate warehouse operations, ensuring greater efficiency and cost reduction. Through Aggregate Programming, the robots coordinate navigation and safety management.
The project was tested at the Leroy Merlin store in Turin to improve warehouse efficiency and inventory management. Autonomous robots equipped with advanced sensors, including lidar and cameras, were used to navigate and identify products on shelves. A physical onboard controller managed robot movements, while the management software used Aggregate Programming to synchronise the robots and communicate with the main controller overseeing operations. A dashboard, developed by the University of Turin’s Computer Science Department, allowed real-time monitoring, and the AI provided precise product recognition. This innovative solution reduced environmental impact, improved safety, and enhanced the efficiency of logistical operations.
The solution
With the integration of 5G connectivity, the robots could exchange real-time information and coordinate efficiently, reducing product handling times.
Compared to its earlier version (ROBOBASE), the robots in the RoboMAG project are equipped with a camera enabling a real-time AI-based system for automatic shelf product recognition. Sensors monitor temperature, humidity, and pressure within the warehouse, ensuring proper storage conditions for products on shelves.
Results
The key evaluation metrics for the RoboMAG system are:
- Effectiveness of Aggregate Programming (AP). Measures the accuracy and autonomous coordination of robots during warehouse operations.
- Performance of the 5G Network. Evaluates the speed, latency, and stability of the connection to ensure reliable communication between robots and devices.
- User Experience Quality. Based on operator feedback, this measures ease of use and the effectiveness of VR simulations for training.
- System Robustness. Assesses the system’s ability to adapt to unforeseen situations and its resilience to faults or interruptions.
To date, the robots have successfully passed tests on all four evaluation points.
RoboMAG is a project by
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