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A Multi-target Path Planning Method Based on Fast Random Search Tree

A random search and path planning technology, applied to road network navigators, navigation calculation tools, etc., can solve problems such as non-optimal paths, and achieve the effect of fast exploration and high efficiency

Active Publication Date: 2020-07-24
ZHEJIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the RRT algorithm is highly random, and the path is usually not optimal, so it needs to be optimized from multiple angles

Method used

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  • A Multi-target Path Planning Method Based on Fast Random Search Tree
  • A Multi-target Path Planning Method Based on Fast Random Search Tree
  • A Multi-target Path Planning Method Based on Fast Random Search Tree

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Embodiment Construction

[0043] The present invention will be further described in conjunction with the specific implementation process below.

[0044] Preparations: The environment map is 480×480, and the starting point is set to X start =(50,40), represented by a five-pointed star point, the end point is X end =(60,430), represented by the circle point, and set four target points X 1 =(440,410),X 2 =(420,80),X 3 =(240,320),X 4 =(200,100), which are represented by four asterisk points. The location and size of obstacles are set as figure 1 shown.

[0045] Step 1: Initialize a tree for each target point, and determine the perception distance with the information in the global map. The calculation method is as follows:

[0046] R=min{D ij},1≤i,j≤n

[0047] where R stands for perception distance, D ij Represents the obstacle-ignoring Euclidean space distance between the root node of the i-th tree and the root node of the j-th tree, and n represents the total number of trees, that is, the tota...

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Abstract

The invention relates to a multi-target point path planning method based on a fast random search tree, and belongs to the field of robot path planning. The method of the invention uses a two-layer tree structure. The bottom layer tree is composed of multiple trees extending from multiple target points, wherein each tree has a weight determined by the surrounding environment, and each tree searchesthe free space by using a fast random search tree algorithm. An effective collision-free path is generated when two trees are close enough, and the effective path and nodes constituting the effectivepath will be transferred to the top layer tree. The top layer tree carries out re-planning on the paths and the nodes by using an improved minimum spanning tree algorithm and finally acquires the shortest path capable of traversing multiple target points. The multi-target point path planning method provided by the invention can be effectively implemented in various obstacle environments. In addition, the calculation speed is high, and the path can be directly navigated by a mobile robot.

Description

technical field [0001] The invention belongs to the field of path planning of mobile robots, and in particular relates to a multi-object point path planning method based on fast random search tree. Background technique [0002] The path planning technology of mobile robots is one of the core technologies of intelligent robots, and it is the basis for mobile robots to complete other tasks. The research on path planning technology has a long history, and the existing technologies can be roughly divided into three categories: one is the path planning algorithm based on graph search, such as Dijistra algorithm, A* algorithm, etc.; the other is population-based optimization Algorithms, such as genetic algorithm, particle swarm algorithm, etc.; the latest one is the path planning algorithm based on sampling, such as the random roadmap algorithm (Probabilistic Roadmaps, PRM) and the rapid random search tree algorithm (Rapidly Random-exploring Tree, RRT). [0003] The so-called mul...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01C21/20G01C21/34
Inventor 樊臻白天刘妹琴张森林何衍
Owner ZHEJIANG UNIV
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