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Dynamic step length-based target gravity bidirectional RRT path planning method

A technology of dynamic step size and path planning, applied in navigation computing tools and other directions, can solve the problems of reducing the randomness of path search, poor real-time performance, and unreachable targets, and achieve the effect of improving the success rate of path search and enhancing the ability to avoid obstacles.

Pending Publication Date: 2018-12-11
KUNMING UNIV OF SCI & TECH
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AI Technical Summary

Problems solved by technology

[0004] Although the two-way RRT algorithm can solve problems such as environmental constraints, time constraints, the dynamic constraints of the robot itself, and high-dimensional space path planning, because this method requires random sampling of the global space, it results in poor real-time performance and low efficiency; based on the idea of ​​target gravity The two-way RRT algorithm can reduce the randomness of path search, but in the environment with many obstacles, there is a problem that the target cannot be reached, the efficiency is low, and the planned path cannot reach the optimal or suboptimal solution.

Method used

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  • Dynamic step length-based target gravity bidirectional RRT path planning method
  • Dynamic step length-based target gravity bidirectional RRT path planning method
  • Dynamic step length-based target gravity bidirectional RRT path planning method

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Embodiment 1

[0025] Embodiment 1: as Figure 1-8 As shown, a dynamic step-based two-way RRT path planning method for target gravity, its main flow chart is as follows figure 1 As shown, the specific steps of the method are as follows:

[0026] Step 1. Build the working environment of the intelligent robot and set it in a 1000×1000 environment, such as figure 2 As shown in , the static obstacles are randomly distributed in black irregular shapes, with different sizes;

[0027] Step 2: Determine the initial point Xinit position and the target point Xgoal position of the intelligent robot in the state space C. figure 2 The lower left corner [0,0] is the initial point Xinit of the intelligent robot, and the upper right corner [1000,1000] is the target point Xgoal of the intelligent robot. Among them, the state space C refers to the working environment; the state space C is composed of the free space Xfree and the obstacle space Xobs, the obstacle space Xobs is composed of static obstacles...

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Abstract

The invention relates to a dynamic step length-based target gravity bidirectional RRT path planning method, and belongs to the field of intelligent robot path planning. A dynamic step length concept is introduced based on a bidirectional RRT algorithm of target gravity idea, and the new node Xnew of an RRT random tree is extended by utilizing the dynamic change of the step length, which can not only utilize the randomness of the bidirectional RRT algorithm, but also utilize the characteristic that the new node Xnew is extended toward the target point Xgoal direction by utilizing the target gravity idea, and simultaneously solves the problem of target unreachability in the multi-obstacle environment, and can effectively improve the path search success rate and enhance the obstacle avoidanceability.

Description

technical field [0001] The invention relates to a dynamic step-based bidirectional RRT path planning method for target gravity, belonging to the field of path planning for intelligent robots. Background technique [0002] Intelligent robots will inevitably encounter various obstacles during their movement, and avoiding these obstacles flexibly and in real time is a key indicator to measure their performance. Intelligent robots with obstacle avoidance functions have high social value and are widely used in aerospace, military, manufacturing, medical and other fields. Path planning technology is one of the core issues in the field of intelligent robots, and has received extensive attention from the outside world in recent years. [0003] The working environment of intelligent robots is becoming more and more complex. Basically, there are a large number of irregular obstacles and uneven distribution of obstacles in the complex environment, including fixed obstacles and moving ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/20
CPCG01C21/20
Inventor 胡兵向凤红毛剑琳郭宁
Owner KUNMING UNIV OF SCI & TECH
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