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Unmanned vehicle curvature continuous path planning method based on fast expansion random tree

A path planning and random tree technology, which is applied to road network navigators, navigation, instruments, etc., can solve the problems of low efficiency of curvature-constrained planning, achieve the effect of avoiding re-planning problems, improving the success rate of expansion, and the value of important engineering applications

Active Publication Date: 2021-06-11
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0005] In order to avoid re-planning caused by collisions caused by traditional path planning algorithms in the process of smoothing post-processing, it is a good solution to explicitly consider the smoothing problem in the planning process, but due to the limit of the edge length in the curvature constraint detection, the traditional The path planning method of the extended random tree considers the planning efficiency of the curvature constraint in the planning process is very low

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  • Unmanned vehicle curvature continuous path planning method based on fast expansion random tree
  • Unmanned vehicle curvature continuous path planning method based on fast expansion random tree
  • Unmanned vehicle curvature continuous path planning method based on fast expansion random tree

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Embodiment 1

[0060] Using matlab as the simulation platform, such as Figure 5 The map shown was used for the simulation, where the black squares are obstacles X obs , the white area is the safe area X free ;Start point x start =(30,80); target point x goal =(180,80);X goal is the point x goal A circular area with a radius of 8 as the center. Figure 6 The execution logic of the simulation program is shown, and the following are the specific execution steps:

[0061] Step 1: Obtain the relevant parameters required by the algorithm, including: starting point x start , target area X goal , environment X=X obs ∪X free , where X obs is the area where the obstacle is located, X free is an obstacle-free area, so there is X obs ∩X free = φ. At the same time, initialize the parameters in the algorithm, including: the longest simulation time T max =30s, the initialization simulation time time=0s, the termination condition threshold D of the dichotomy dichotomy =1.5, given upper limi...

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Abstract

The invention relates to an unmanned vehicle curvature continuous path planning method based on a fast expansion random tree, and belongs to the technical field of path planning. Nodes which can be in collision-free connection are recorded by adopting the node list, and the nodes are taken out from the list in an inverted order by using a stack thought to perform curvature constraint detection, so that a feasible path which not only conforms to curvature constraint but also is lower in path length is obtained. According to the method, the expansion success rate of the random tree can be greatly improved, and in addition, due to the fact that the expandability of the nodes is considered in the smooth scheme design, the length of the path planned by the algorithm will be gradually reduced along with planning.

Description

technical field [0001] The invention belongs to the technical field of path planning, and in particular relates to a path planning method based on rapidly expanding random trees, so that the curvature of the planned path is continuous and satisfies the maximum curvature constraint. There are a wide range of applications. Background technique [0002] Intelligent robots provide convenience and efficiency for life and production, making it a hot topic in today's society, and motion planning is one of the key technologies for robot movement, especially in today's increasingly intense demand for unmanned vehicle technology, a feasible collision-free The path is the key to the safe movement of the robot. In existing planning techniques, graph search algorithms can be used to obtain the shortest path, but the planning speed of such algorithms is greatly affected by the size of the environment. Rapidly expanding random tree (RRT / rapidlyexploring random tree) algorithm abandons th...

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Application Information

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IPC IPC(8): G01C21/34
CPCG01C21/3446
Inventor 廖彬宗意棚朱沈瑞马瑞瑞刘光辉万方义
Owner NORTHWESTERN POLYTECHNICAL UNIV