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

Abstract

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.

Optimizing Energy Consumption of Industrial Robots with Model-Based Layout Design

“…In the study [25], the authors apply metaheuristic theories (genetic algorithm and particle swarm optimization) to obtain energy efficiency in different dual-arm robot configurations for part retrieval and placing on a conveyor belt. Neural network methods are used in [26] to identify non-linear dependencies in EC models for industrial robots that perform processes characterized by complex trajectories: palletizing packages, loading/unloading of machines, and contact welding. These models are then used to adjust motion parameters (speed, acceleration) to minimize EC.…”