Energy-Efficient and Fault-Tolerant Control of a Six-Axis Robot Based on AI Models

Patryk Nowak () and Zoran Pandilov
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Patryk Nowak: Department of Mechatronic Devices, Poznan University of Technology, 60-965 Poznan, Poland
Zoran Pandilov: Faculty of Mechanical Engineering, Ss. Cyril and Methodius University in Skopje, Karpos II b.b., P.O. Box 464, MK-1000 Skopje, North Macedonia

Energies, 2024, vol. 18, issue 1, 1-23

Abstract: This paper describes the task of controlling a robot to enable energy savings in the case of one- or two-axis failure. The proposed algorithms are tested in a task similar to “pick and place” but without gripping. Obstacles are present in the robot’s workspace. The goal of the algorithm is to control the robot in such a way that energy consumption is minimized while also avoiding obstacles and ensuring fault tolerance in the event of axis failures. The algorithm uses a developed torque model of the robot, which is employed to calculate the energy requirements for each possible movement step in the robot’s position. In the robot’s control system, artificial intelligence methods are also applied. Specifically, a genetic algorithm is used to generate learning data for the selection of the optimal kinematic configuration of the robot, and a multilayer perceptron is utilized to predict the parameters of the defined reward function. This function is crucial for selecting the optimal action at each time step. The study demonstrates that the application of the algorithm leads to a reduction in robot energy consumption. Studies conducted in simulation and verified on a real robot for 10 different obstacle and target positions and 22 possible kinematic configurations of the robot, consisting of all axes active, any one axis inactive, or any two axes inactive, confirm the energy-saving possibilities.

Keywords: fault-tolerant control; energy optimization path; obstacle avoidance (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: 2024
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