Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots

Abstract

Mobile robots have an important role in material handling in manufacturing and can be used for a variety of automated tasks. The accuracy of the robot’s moving trajectory has become a key issue affecting its work efficiency. This paper presents a method for optimizing the trajectory of the mobile robot based on the digital twin of the robot. The digital twin of the mobile robot is created by Unity, and the trajectory of the mobile robot is trained in the virtual environment and applied to the physical space. The simulation training in the virtual environment provides schemes for the actual movement of the robot. Based on the actual movement data returned by the physical robot, the preset trajectory of the virtual robot is dynamically adjusted, which in turn enables the correction of the movement trajectory of the physical robot. The contribution of this work is the use of genetic algorithms for path planning of robots, which enables trajectory optimization of mobile robots by reducing the error in the movement trajectory of physical robots through the interaction of virtual and real data. It provides a method to map learning in the virtual domain to the physical robot.

Self-Tuning Control of Manipulator Positioning Based on Fuzzy PID and PSO Algorithm

“…Now, more and more intelligent algorithms are used to manipulator control systems (Zhang et al, 2017;Lu, 2018;Jiang et al, 2019b;Huang et al, 2019;Nguyen et al, 2019;Huang et al, 2020;Ozyer, 2020;Sun and Liu, 2020;Liu et al, 2021c;Yu et al, 2021). Due to various factor interferences, such as the environment, the robot cannot be positioned accurately, and the positioning error will gradually increase (Sun et al, 2020b;Ozyer, 2020;Liu X. et al, 2021). Against the problem of uncertainty in the motion control of the manipulator, the manipulator is controlled to obtain the desired position by means of a calculated torque method (He et al, 2019;My and Bein, 2020), which improves the systematic robust to a certain extent.…”

Intelligent Detection of Steel Defects Based on Improved Split Attention Networks

“…Good performance of ResNet on image recognition and localization tasks showed that characterisation depth is of central importance for many visual recognition tasks. In the following years, excellent feature extraction networks such as ResNeXt, DenseNet, RegNet, SEnet, SKNet, etc ( Xie et al, 2017 ; Liu X. et al, 2021 ; Liu et al, 2021b ; Liu et al, 2021c ; Liu et al, 2021d ; Sun et al, 2020c ; Sun et al, 2020d ). were successively proposed, constantly refreshing the accuracy rate of tasks such as image classification.…”

Attitude Stabilization Control of Autonomous Underwater Vehicle Based on Decoupling Algorithm and PSO-ADRC

“…The Active Disturbance Rejection Control (ADRC) algorithm studied in this paper ( Han, 2002 ; Ma et al, 2020 ; Sun et al, 2021 ; Tao et al, 2022b ), which inherits the characteristics of the PID algorithm, does not depend on the accurate model of the control object, while using modern signal processing techniques to improve its control process, unifies external disturbances and system errors as total disturbances, and then estimates and compensates them, which has the advantage of being easy to use in engineering and at the same time has a strong anti-disturbance capability. Depending on the Particle Swarm Optimization (PSO) ( Wang et al, 2013 ; Liu X. et al, 2021 ; Bai et al, 2022 ; Liu et al, 2022b ) which is characterized by fast convergence and high computational efficiency ( Li et al, 2019b ; Jiang et al, 2021b ; Liu et al, 2021c ; Huang et al, 2021 . ), Optimizing the parameters of ADRC using PSO can achieve better control effect.…”