Research topics deal with intelligent manufacturing and Industry 4.0, with the main aim to add industrial robots the capability to self-optimize their behavior, in order to achieve the best performance in any condition. Using model-based approaches, robot cells and plants rely on a digital twin to improve their energy efficiency, accuracy and productivity.


Prof. Marcello Pellicciari


Fp7 AREUS – Automation and Robotics for European Sustainable Manufacturing

H2020 COLROBOT –  Collaborative Robotics for Assembly and Kitting in Smart Manufacturing Cluster Adaptive.


  1. Pellicciari M Berselli G Leali F Vergnano A “A method for reducing the energy consumption of pick-and-place industrial robots” Mechatronics, vol. 23, issue 3 (2013)
  2. Lehmann C, Pellicciari, M Drust, M Gunnink JW, “Machining with industrial robots: The COMET project approach” Communications in Computer and Information Science, vol. 371 (2013)
  3. Meike D Pellicciari M Berselli G “Energy efficient use of multi-robot production lines in the automotive industry: Detailed system modeling and optimization” IEEE Transactions on Automation Science and Engineering, vol. 11, issue 3 (2014)
  4. Gadaleta M Pellicciari M Berselli G “Energy-optimal layout design of robotic work cells: Potential assessment on an industrial case study” Robotics and Computer-Integrated Manufacturing, vol. 47 (2017)
  5. Peruzzini M Pellicciari M “A framework to design a human-centred adaptive manufacturing system for aging workers” Advanced Engineering Informatics, vol. 33 (2017).