Automated method based on a neural network model for searching energy-efficient complex movement trajectories of industrial robot in a differentiated technological process

Frontiers in Energy Research

Energy Research

Markel A. Melnichenko¹  Valeria P. Egorova¹  Aleksandr I. Gorkavyy¹  Mikhail A. Gorkavyy² 

[1] Faculty of Energy and Management, Komsomolsk-na-Amure State University, Komsomolsk-on-Amur, Russia;null;

关键词: technological process;    robotization;    food industry;    industrial robot;    neural network model;    energy efficiency;    optimization;   

DOI  :  10.3389/fenrg.2023.1129311

 received in 2022-12-21, accepted in 2023-03-07,  发布年份 2023

来源: Frontiers

【 摘 要 】

Purpose of the work: Researching possibility of creating a method for improving the energy efficiency of differentiated robotic technological process (RTP) in the food industry. The high rates of development of production processes robotization, including in the food industry, leading to increase the cost of electrical energy, determine the research tasks relevance in finding energy-efficient methods for controlling industrial robots.Methodology: The proposed approach is based on principles of object-oriented design and possibility of classifying robotic technological process: Applying specific model sets and methods to improve energy efficiency to individual classes. The existing possibility of energy consumption synthesizing models of robots inside differentiated robotic technological process, such as stacking loads, in reduced form, made it possible to use neural network methods to identify non-linear dependencies. At the same time, training sample for intelligent modules was formed on the basis of classical experiment planning algorithms. The synthesis of methods, models and procedures was implemented on the basis of high-level programming languages C++, MATLAB.Results: A mathematical model and automated algorithms for its synthesis are proposed, which make it possible to adjust robotic technological process simulation model taking into account its specifics and implement a method for finding the optimal parameters of its functioning. To confirm the effectiveness of proposed solution, the obtained neural network model and optimization method were tested on real robotic technological process, and the calculation of economic efficiency of proposed solution was also given.Conclusion/recommendations: The application of this approach will significantly reduce energy costs for robotic operations in the food industry.

【 授权许可】

Unknown   
Copyright © 2023 Gorkavyy, Gorkavyy, Egorova and Melnichenko.