The project will develop advanced algorithms for automated planning and distributed con­ trol and will closely work with UC4. These algorithms will be tailored to handle dynamic and complex industrial environments such as assembly lines and automated warehouses. To achieve this, sophisti­ cated models for task scheduling and workload distribution will be created, which can adjust to changes in the production environment dynamically. Machine learning principles will be incorporated, enabling the robots to learn and optimise their performance based on past operations. Real-time re-planning is a crucial component of the project's algorithms. This feature will allow the system to quickly adapt to unforeseen changes in production schedules or resource availability. To complement this, distributed control mechanisms will be developed, ensuring efficient coordination among multiple robots

Expected outcomes:

The project aims to create a highly efficient and autonomous multi-robot system that improves industrial operations productivity, flexibility, and precision. The system will demonstrate the practical benefits of advanced automated planning and distributed control in industrial robotics, leading to a significant advancement in manufacturing technologies.

Staffing: : 1 PhD student, 2 Assoc. Sr. Lecturers (part of), 1 external PhD student (fully funded outside of MARC by Bosch Corporate Research, Germany

Duration: 2024-2029

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