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Improved ant colony algorithm and dynamic window method-based hybrid algorithm applied to intelligent vehicle path planning

A path planning, dynamic window technology, applied in vehicle position/route/height control, motor vehicle, non-electric variable control and other directions, can solve the problems of many convergence iterations, falling into local optimum, etc., to achieve good robustness, enhanced Effects of Accuracy, Good Robustness, and Accuracy

Inactive Publication Date: 2020-09-22
SHANDONG JIAOTONG UNIV
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Problems solved by technology

[0006] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is to solve the problems that the smart car has many convergence iterations in path planning, is easy to fall into local optimum, and cannot realize obstacle avoidance for local obstacles in real time, and improves the algorithm Robustness and accuracy to achieve automatic feedback control of smart cars

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  • Improved ant colony algorithm and dynamic window method-based hybrid algorithm applied to intelligent vehicle path planning
  • Improved ant colony algorithm and dynamic window method-based hybrid algorithm applied to intelligent vehicle path planning
  • Improved ant colony algorithm and dynamic window method-based hybrid algorithm applied to intelligent vehicle path planning

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[0051] The following describes several preferred embodiments of the present invention with reference to the accompanying drawings, so as to make the technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned herein.

[0052] The hybrid algorithm based on the improved ant colony algorithm and the dynamic window method proposed by the present invention, which is applied to the path planning of the smart car, can realize the real-time obstacle avoidance of the smart car, smooth the turning angle of the path, improve the planning efficiency of the path, and realize the automatic control of the smart car. feedback control. The flow chart of the mixed algorithm based on the improved ant colony algorithm and the dynamic window method proposed by the present invention is as follows figure 1 As shown, the specific steps ar...

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Abstract

The invention discloses an improved ant colony algorithm and dynamic window method-based hybrid algorithm applied to intelligent vehicle path planning. The algorithm can realize real-time obstacle avoidance, improve the path planning efficiency and realize automatic feedback control of an intelligent vehicle. According to the algorithm, after the Matlab simulation platform and the ROS-based intelligent vehicle platform are used for specifying a starting point and a target point on the premise that the obstacle is known, global path planning of the intelligent vehicle is completed, meanwhile,unknown obstacles are set on the grid map, local moving target points are planned, the local target points are tracked in real time, and local real-time obstacle avoidance of the intelligent vehicle is completed. According to the intelligent vehicle path planning method based on the improved ant colony algorithm and the dynamic window method, the turning angle of the path is smoothened, the path planning efficiency is improved, real-time obstacle avoidance of the intelligent vehicle is achieved, and good robustness and accuracy are achieved.

Description

technical field [0001] The invention relates to a hybrid algorithm based on an improved ant colony algorithm and a dynamic window method applied to intelligent vehicle path planning, and belongs to the field of intelligent vehicle path planning. Background technique [0002] With the rapid development of computer science and technology, the research of intelligent vehicles has become one of the research hotspots of intelligent vehicle transportation system. Among them, path planning is an important technology in the field of intelligent vehicle research, which aims to find the path from the starting point to the target in the obstacle environment. The collision-free path of the point. [0003] At present, scholars at home and abroad have successively developed several algorithms for solving path planning problems, such as global path planning algorithms: Voronoi diagram, Dijkstra algorithm, genetic algorithm, ant colony algorithm, etc. Among them, the ant colony algorithm i...

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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0223G05D1/0214G05D1/0221G05D1/0276
Inventor 李爱娟陈政宏李韶华王希波黄欣邱绪云王健徐传燕韩文尧葛庆英王春民
Owner SHANDONG JIAOTONG UNIV
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