Human-robot collaboration has become a key driver for manufacturing sustainability in Europe. Thanks to the many advantages that a fenceless, shared working environment offers, industry has recently grown a particular interest in collaborative robotics, thus sustaining the transition from academy to factories of this technology. In this work, we present a robotic solution integrating a serial manipulator and a mobile platform, both characterized by collaborative features, for feeding raw material to a packaging automatic machine. The goal is to provide an overview of such a complex system from different points of view, including hardware and software architecture, trajectory planning, computer-vision strategies, customized mechanical design, and field validation. We will describe the obtained results and the lessons learned, and provide an outlook for future evolution.
Mobile cobots for autonomous raw-material feeding of automatic packaging machines / Comari S.; Di Leva R.; Carricato M.; Badini S.; Carapia A.; Collepalumbo G.; Gentili A.; Mazzotti C.; Stagliano K.; Rea D.. - In: JOURNAL OF MANUFACTURING SYSTEMS. - ISSN 0278-6125. - STAMPA. - 64:(2022), pp. S0278612522001029.211-S0278612522001029.224. [10.1016/j.jmsy.2022.06.007]
Mobile cobots for autonomous raw-material feeding of automatic packaging machines
Comari S.
;Di Leva R.;Carricato M.
;
2022
Abstract
Human-robot collaboration has become a key driver for manufacturing sustainability in Europe. Thanks to the many advantages that a fenceless, shared working environment offers, industry has recently grown a particular interest in collaborative robotics, thus sustaining the transition from academy to factories of this technology. In this work, we present a robotic solution integrating a serial manipulator and a mobile platform, both characterized by collaborative features, for feeding raw material to a packaging automatic machine. The goal is to provide an overview of such a complex system from different points of view, including hardware and software architecture, trajectory planning, computer-vision strategies, customized mechanical design, and field validation. We will describe the obtained results and the lessons learned, and provide an outlook for future evolution.File | Dimensione | Formato | |
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