scholarly journals Research on Intelligent Vehicle Path Planning Based on Rapidly-Exploring Random Tree

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yangyang Shi ◽  
Qiongqiong Li ◽  
Shengqiang Bu ◽  
Jiafu Yang ◽  
Linfeng Zhu
Keyword(s):  
Convergence Speed ◽  
Random Tree ◽  
Tree Search ◽  
Vehicle Model ◽  
Turning Angle ◽  
Tree Algorithm ◽  
Slow Convergence ◽  
Joint Simulation ◽  
Simulation Based ◽  
Vehicle Path

Aiming at the problems of large randomness, slow convergence speed, and deviation of Rapidly-Exploring Random Tree algorithm, a new node is generated by a cyclic alternating iteration search method and a bidirectional random tree search simultaneously. A vehicle steering model is established to increase the vehicle turning angle constraint. The Rapidly-Exploring Random Tree algorithm is improved and optimized. The problems of large randomness, slow convergence speed, and deviation of the Rapidly-Exploring Random Tree algorithm are solved. Node optimization is performed on the generated path, redundant nodes are removed, the length of the path is shortened, and the feasibility of the path is improved. The B-spline curve is used to insert the local end point, and the path is smoothed to make the generated path more in line with the driving conditions of the vehicle. The feasibility of the improved algorithm is verified in different scenarios. MATLAB/CarSim is used for joint simulation. Based on the vehicle model, virtual simulation is carried out to track the planned path, which verifies the correctness of the algorithm.

scholarly journals Building a Classifier of Onset Stroke Prediction Using Random Tree Algorithm

2017 ◽  
Vol 7 (4) ◽  
pp. 61-66 ◽  
Author(s):  
Yu-Chen Chen ◽  
◽  
Takashi Suzuki ◽  
Masaaki Suzuki ◽  
Hiroyuki Takao ◽  
...  
Keyword(s):  
Random Tree ◽  
Tree Algorithm

Research on Application of Ant Colony Optimization

2014 ◽  
Vol 548-549 ◽  
pp. 1213-1216
Author(s):  
Wang Rui ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Slow Convergence ◽  
Simulation Results ◽  
Research On Application

We research on application of ant colony optimization. In order to avoid the stagnation and slow convergence speed of ant colony algorithm, this paper propose the multiple ant colony optimization algorithm based on the equilibrium of distribution. The simulation results show that the optimal algorithm can have better balance in reducing stagnation and improving the convergence.

The Study on Co-Simulation Based Tracked Vehicle Path Tracking Control

2015 ◽  
Author(s):  
Can Wang ◽  
Bo Yang ◽  
Gangfeng Tan ◽  
YiRui Wang ◽  
Li Zhou
Keyword(s):  
Tracking Control ◽  
Path Tracking ◽  
Tracked Vehicle ◽  
Simulation Based ◽  
Vehicle Path

An Improved Genetic Algorithm for Digital Facility Layout Optimization Oriented Multi-Species and Variable-Batch Production Mode

2013 ◽  
Vol 694-697 ◽  
pp. 3632-3635
Author(s):  
Dao Guo Li ◽  
Zhao Xia Chen
Keyword(s):  
Genetic Algorithm ◽  
Facility Layout ◽  
Optimal Solution ◽  
Convergence Speed ◽  
Improved Genetic Algorithm ◽  
Layout Problem ◽  
Batch Production ◽  
Production Mode ◽  
Slow Convergence ◽  
Weak Points

When solving facility layout problem for the digital workshop to optimize the production, the traditional genetic algorithm has its flaws with slow convergence speed and that the accuracy of the optimal solution is not ideal. This paper analyzes those weak points and proposed an improved genetic algorithm according to the characteristics of multi-species and variable-batch production mode. The proposed approach improved the convergence speed and the accuracy of the optimal solution. The presented model of GA also has been tested and verified by simulation.

scholarly journals An Empirical Methodology to Examine the Effect of Meta Classifiers in J48 and Random Tree in Weather Data

2020 ◽  
Vol 9 (6) ◽  
pp. 153-157
Keyword(s):  
Human Activity ◽  
Data Interpretation ◽  
Random Tree ◽  
Weather Data ◽  
Peninsular India ◽  
Tree Algorithm ◽  
Implementation Phase ◽  
Empirical Methodology

Weather data interpretation has become vitally important in most domains of human activity and this is because in recent years, major changes have begun to impact climate globally – peninsular India is among the regions seriously affected with this and prediction has become a particularly urgent concern. In this work to bring out a better methodology to examine the weather data using Meta classifiers, a method is postulated by formulating it with Tree classifiers – J48 and Random Tree. Implementation phase has shown distinct results for both the classifiers. Regardless, we could conclude from this work that the effect of Meta Classifiers in J48 and Random Tree algorithm shows that efficiency can be improved by applying the same.

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