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

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 incr...

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Published in:Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-14
Main Authors: Yang, Jiafu, Bu, Shengqiang, Li, Qiongqiong, Shi, Yangyang, Zhu, Linfeng
Format: Article
Language:English
Subjects:
Algorithms
B spline functions
Computer simulation
Convergence
Deviation
Driving conditions
Efficiency
Feasibility
Intelligent vehicles
Iterative methods
Mathematical problems
Optimization
Path planning
Randomness
Steering
Vehicles
Velocity
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cited_by cdi_FETCH-LOGICAL-c360t-2b9e8e1c3ea01b0cb65ad348c50d1bfd782d68b57ae1b5949ff2d95030350cfe3
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container_end_page 14
container_issue 2020
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container_title Mathematical problems in engineering
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creator Yang, Jiafu
Bu, Shengqiang
Li, Qiongqiong
Shi, Yangyang
Zhu, Linfeng
description 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.
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subjects Algorithms
B spline functions
Computer simulation
Convergence
Deviation
Driving conditions
Efficiency
Feasibility
Intelligent vehicles
Iterative methods
Mathematical problems
Optimization
Path planning
Randomness
Steering
Vehicles
Velocity
title Research on Intelligent Vehicle Path Planning Based on Rapidly-Exploring Random Tree
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