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Unmanned vehicle dynamic path planning method based on free space and fast search random tree algorithm

A free space, dynamic path technology, used in surveying and navigation, road network navigators, measuring devices, etc., can solve the problem of inapplicability, complex free space logic, artificial potential field method and particle swarm algorithm easy to fall into the local optimal solution And other issues

Pending Publication Date: 2021-05-25
BEIHANG UNIV
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Problems solved by technology

[0005] Existing path planning algorithms are difficult to meet the above requirements at the same time, for example, RRT algorithm and A* algorithm are easy to fall into the trap area, and the planning speed is slow in complex environments; artificial potential field method and particle swarm algorithm are easy to fall into the local optimal solution ; The grid method is easily affected by the accuracy of the grid division; the V-graph algorithm and the tangent method are not suitable for complex maps; Applicable to local fast planning in dynamic environment

Method used

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  • Unmanned vehicle dynamic path planning method based on free space and fast search random tree algorithm
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  • Unmanned vehicle dynamic path planning method based on free space and fast search random tree algorithm

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Embodiment Construction

[0021] 5.1 Building the map as simply connected polygons

[0022] The basic idea of ​​the free space method is to divide the map into several free spaces composed of convex polygons. This paper uses a concave polygon convex decomposition algorithm. Therefore, the map with obstacles should be converted into a polygon first. The basic idea and steps are as follows .

[0023] Such as figure 1 As shown, when the map M 1 m 2 …M n There is an obstacle O in 1 o 2 …O n , choose a point O of the obstacle i A certain vertex M on the boundary of the map i connection, denoted as O i m i . Its vector can have two directions and Assuming two vectors and There is a distance ΔD→0 between them, and the vertex O of the obstacle i Vertex M of the map with i Connected by two vectors, the map becomes a simply connected domain map M 1 m 2 …M i o i o i+1 …O n o 1 …O i m i …M n . If there are multiple polygonal obstacles in the map, each obstacle needs to be directly o...

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Abstract

The invention discloses an unmanned vehicle dynamic path planning method based on a free space and a fast search random tree algorithm. A method for constructing a free space on a barrier-containing map is designed by utilizing a concave polygon convex decomposition graphics method, and an artificial bee colony algorithm is applied to carry out path optimization to find out a global optimal path. The fast path planning method based on the improved fast search random tree algorithm is realized by adjusting the random tree node sampling probability. Finally, global path planning of a dynamic map and local rapid path planning in the unmanned vehicle advancing process are achieved through the free space method and the rapid search random tree algorithm, and the quality and speed of path planning in the dynamic environment are both considered.

Description

[0001] 1. Technical field [0002] The invention relates to the field of path planning, in particular to path planning in which a map changes during the driving of an unmanned vehicle in a dynamic environment. [0003] 2. Background technology [0004] Autonomous driving technology is a hot topic in the field of artificial intelligence. In the future society, most vehicles will be equipped with autonomous driving technology, which will make the ground traffic smoother and the traffic accident rate lower. As a key part of the dynamic environment path planning method, it is necessary to achieve the following objectives and requirements: 1) the driving path does not collide with obstacles; 2) the path must connect the starting point and the end area; 3) the path quality is high; 4) dynamic Planning time is short. [0005] Existing path planning algorithms are difficult to meet the above requirements at the same time, for example, RRT algorithm and A* algorithm are easy to fall in...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/34
CPCG01C21/3446G01C21/3415
Inventor 李昭莹石若凌
Owner BEIHANG UNIV
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