scholarly journals Path Planning of Hydraulic Support Pushing Mechanism Based on Extreme Learning Machine and Descartes Path Planning

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 97
Author(s):  
Suhua Li ◽  
Jiacheng Xie ◽  
Xuewen Wang ◽  
Fang Ren ◽  
Xin Zhang ◽  
...  
Keyword(s):  
Path Planning ◽  
Extreme Learning Machine ◽  
Target Location ◽  
Industrial Robot ◽  
Prediction Method ◽  
Local Error ◽  
Hydraulic Support ◽  
Key Points ◽  
Scraper Conveyor ◽  
Learning Machine

As a floating system connecting hydraulic support and scraper conveyor, the path planning of pushing mechanism is of great significance for their coordinated movement. In this paper, a method for path planning of hydraulic support pushing mechanism based on extreme learning machine (ELM) and Descartes path planning is proposed. According to the motion characteristics of moving mechanism, it is transformed into industrial robot model, based on the characteristics of the coordinates of the key points on the ear seat of the scraper conveyor when advancing, a prediction method of the key points coordinates based on ELM is proposed, so the target location of the end-effector is obtained. The path of the joint is determined by polynomial path partition and Descartes path planning method. The path is modified by Gaussian filtering method, and the peak value of path obtained by planning is filtered out, and the path correction is realized. Finally, the virtual simulation test is carried out in Unity3D. The planned coordinate curve has Poisson-like distribution and approximately around the target coordinate curve, and local error and correction error are within 2 cm and 0.1 cm, respectively. The coordinate curve obtained by combining planning and correction has a better effect.

scholarly journals Virtual straightening of scraper conveyor based on the position and attitude solution of industrial robot model

2021 ◽  
Author(s):  
Suhua Li ◽  
Jiacheng Xie ◽  
Fang Ren ◽  
Xin Zhang ◽  
Xuewen Wang ◽  
...  
Keyword(s):  
Coal Seam ◽  
Industrial Robot ◽  
Steering Committee ◽  
High Accuracy ◽  
Simulation System ◽  
Direct Proportion ◽  
Hydraulic Support ◽  
Actual Distance ◽  
Scraper Conveyor ◽  
Comparison And Analysis

AbstractThe movement of the floating connecting mechanism between a hydraulic support and scraper conveyor is space movement; thus, when the hydraulic support pushes the scraper conveyor, there is an error between the actual distance of the scraper conveyor and the theoretical moving distance. As a result, the scraper conveyor cannot obtain the straightness requirement. Therefore, the movement law of the floating connecting mechanism between the hydraulic support and scraper conveyor is analyzed and programmed into the Unity3D to realize accurate pushing of the scraper conveyor via hydraulic support. The Coal Seam + Equipment Joint Virtual Straightening System is established, and a straightening method based on the motion law of a floating connection is proposed as the default method of the system. In addition, a straightening simulation of the scraper conveyor was performed on a complex coal seam floor, the results demonstrate that the average straightening error of the scraper conveyor is within 2–8 mm, and is in direct proportion to the fluctuation of the coal seam floor in the strike of the seam with high accuracy, the straightness of scraper conveyor is more affected by the subsidence terrain during straightening than by the bulge terrain. And some conclusions are verified by experiment. Based on the verification of the relevant conclusions, a comparison and analysis of Longwall Automation Steering Committee (LASC) straightening technology and default straightening method in the simulation system shows that the straightness accuracy of LASC straightening technology under complex floor conditions is slightly less than that of the default straightening method in the proposed system.

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

scholarly journals Prediction method of flow instability based on multi-objective optimized extreme learning machine

2014 ◽  
Vol 63 (20) ◽  
pp. 200505
Author(s):  
Chen Han-Ying ◽  
Gao Pu-Zhen ◽  
Tan Si-Chao ◽  
Fu Xue-Kuan
Keyword(s):  
Extreme Learning Machine ◽  
Flow Instability ◽  
Prediction Method ◽  
Multi Objective ◽  
Learning Machine

scholarly journals Research on Optimization of Improved Gray Wolf Optimization-Extreme Learning Machine Algorithm in Vehicle Route Planning

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Shijin Li ◽  
Fucai Wang
Keyword(s):  
Global Optimization ◽  
Path Planning ◽  
Extreme Learning Machine ◽  
High Efficiency ◽  
Rapid Development ◽  
Route Planning ◽  
Local Optimization ◽  
Gray Wolf ◽  
Learning Machine ◽  
Traffic Optimization

With the rapid development of intelligent transportation, intelligent algorithms and path planning have become effective methods to relieve traffic pressure. Intelligent algorithm can realize the priority selection mode in realizing traffic optimization efficiency. However, there is local optimization in intelligence and it is difficult to realize global optimization. In this paper, the antilearning model is used to solve the problem that the gray wolf algorithm falls into local optimization. The positions of different wolves are updated. When falling into local optimization, the current position is optimized to realize global optimization. Extreme Learning Machine (ELM) algorithm model is introduced to accelerate Improved Gray Wolf Optimization (IGWO) optimization and improve convergence speed. Finally, the experiment proves that IGWO-ELM algorithm is compared in path planning, and the algorithm has an ideal effect and high efficiency.

Sign in / Sign up

Export Citation Format

Share Document