At present, energy consumption strongly affects the financial payback period of industrial robots, as well as the related manufacturing process sustainability. Henceforth, during both design and manufacturing management stages, it becomes crucial to assess and optimize the overall energy efficiency of a robotic cell by means of digital manufacturing tools. In practice, robotic plant designers and managers should be able to provide accurate decisions also aimed at the energy optimization of the robotic processes. The strong scientific and industrial relevance of the topic has led to the development of many solutions but, unfortunately, state of the art industrial manipulators are equipped with closed controllers, which heavily limit the feasibility and performance of most of the proposed approaches. In light of the aforementioned considerations, the present paper presents a novel simulation tool, seamlessly interfaced with current robot offline programming tools used in industrial practices, which allows to automatically compute energy-optimal motion parameters, thus reducing the robot energy consumption, while also keeping the same productivity and manufacturing quality. The main advantage of this method, as compared to other optimization routines that are not conceived for direct integration with commercial industrial manipulators, is that the computed parameters are the same ones settable in the robot control codes, so that the results can automatically generate ready-to-use energy-optimal robot code. Experimental tests, performed on a KUKA Quantec KR210 R2700 prime industrial robot, have confirmed the effectiveness of the method and engineering tool.

Optimization of the energy consumption of industrial robots for automatic code generation / Gadaleta, M.; Pellicciari, M.; Berselli, G.. - In: ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING. - ISSN 0736-5845. - 57:(2019), pp. 452-464. [10.1016/j.rcim.2018.12.020]

Optimization of the energy consumption of industrial robots for automatic code generation

Gadaleta M.;Pellicciari M.;
2019

Abstract

At present, energy consumption strongly affects the financial payback period of industrial robots, as well as the related manufacturing process sustainability. Henceforth, during both design and manufacturing management stages, it becomes crucial to assess and optimize the overall energy efficiency of a robotic cell by means of digital manufacturing tools. In practice, robotic plant designers and managers should be able to provide accurate decisions also aimed at the energy optimization of the robotic processes. The strong scientific and industrial relevance of the topic has led to the development of many solutions but, unfortunately, state of the art industrial manipulators are equipped with closed controllers, which heavily limit the feasibility and performance of most of the proposed approaches. In light of the aforementioned considerations, the present paper presents a novel simulation tool, seamlessly interfaced with current robot offline programming tools used in industrial practices, which allows to automatically compute energy-optimal motion parameters, thus reducing the robot energy consumption, while also keeping the same productivity and manufacturing quality. The main advantage of this method, as compared to other optimization routines that are not conceived for direct integration with commercial industrial manipulators, is that the computed parameters are the same ones settable in the robot control codes, so that the results can automatically generate ready-to-use energy-optimal robot code. Experimental tests, performed on a KUKA Quantec KR210 R2700 prime industrial robot, have confirmed the effectiveness of the method and engineering tool.
57
452
464
Optimization of the energy consumption of industrial robots for automatic code generation / Gadaleta, M.; Pellicciari, M.; Berselli, G.. - In: ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING. - ISSN 0736-5845. - 57:(2019), pp. 452-464. [10.1016/j.rcim.2018.12.020]
Gadaleta, M.; Pellicciari, M.; Berselli, G.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1193865
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