Intelligent Control of Robots with Minimal Power Consumption in Pick-and-Place Operations

Valery Vodovozov (), Zoja Raud and Eduard Petlenkov
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Valery Vodovozov: Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Zoja Raud: Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Eduard Petlenkov: Department of Computer Systems, Tallinn University of Technology, 19086 Tallinn, Estonia

Energies, 2023, vol. 16, issue 21, 1-17

Abstract: In many industries, such as assembling, welding, packaging, quality control, loading, and wrapping, a specific operation is requested, which is to pick processed objects in a given area of the workspace and hold them there for a rather long time compared with picking. The current study aims to minimize the power consumed by robots in pick-and-place applications with long-term placing and short-term picking operations. The main contribution of the paper is in the development of an approach that ensures the low power required by the robot by selecting the best robot joint configuration for object placement and providing intelligent control of robot joints for object-picking. The proposed and tested methodology is based on the mutual solution of the forward kinematics, inverse kinematics, inverse statics, and reinforcement learning problems in robotics. An appropriate neural-network-based controller is designed. In this work, model development, simulation, and experimental stages are described. As a result, several MATLAB/Simulink™ models and simulation methods are designed for efficient robot control and an appropriate neural-network-based controller is developed. The experiment conducted on the IRB1600 robot demonstrates that up to 18% of the consumed power may be saved thanks to an optimally chosen joint configuration.

Keywords: power consumption; robotics problems; robot control; pick-and-place operation; reinforcement learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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