Route planning method based on triangular inner center guided RRT algorithm

Abstract

The invention relates to a path planning method based on a triangular inner center guided RRT algorithm. In order to overcome the defects of an RRT algorithm and avoid the risk of local minimum causedby the fact that a target point appears in a random point at a certain probability, the invention provides a method for guiding a random tree by utilizing the inner center of a triangle. Three vertexes of a triangle are formed by a random point generated by a random function, a point closest to the random point in a random tree and a target point, then an inner center coordinate of the triangle is calculated, and the inner center coordinate is used as a growth direction of the random tree; in addition, under a certain cycle index, the sampling mode is adjusted by recording the number of timesof using the inner core guidance and the growth condition of the random tree. Therefore, not only is the growth of the random tree guided, but also the local minimum risk is effectively avoided. According to the method, the planning efficiency is improved, the time required for planning the path is shorter, the number of iterations is smaller, and the path is shorter.

Description

technical field [0001] The invention relates to the field of path planning, in particular to a path planning method based on a triangular inwardly guided RRT algorithm. Background technique [0002] Path planning has always been a research hotspot in the field of mobile robotics. The main task of path planning is to find a continuous collision-free path from the initial state point to the target state point in space, while satisfying the corresponding conditional constraints. [0003] Commonly used trajectory planning algorithms include graph search algorithm, bionic intelligent algorithm, artificial potential field method, etc. Traditional path planning algorithms, such as the artificial potential field method, are based on vector synthesis, and plan the robot’s motion path under the combined effect of the repulsive force of obstacles on the mobile robot and the attraction of the target position to the mobile robot, but when the attraction When the resultant force with th...

AI Technical Summary

Problems solved by technology

However, because the basic RRT algorithm adopts a uniform sampling strategy and lacks guidance information, the resulting path is tortuous. Under the complex obstacle distribution, the planning time is long; in addition, for the target point to appear as a random point with a certain probability In the sampling point, although this can improve the planning efficiency, it may also lead to falling into a local minimum, and eventually no path can be found under the specified time or number of iterations

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