Intelligent vehicle path planning method based on bidirectional extension random trees

Abstract

The invention discloses an intelligent vehicle path planning method based on bidirectional extension random trees. The method comprises the following steps of generating a curve, and constructing twoextension trees which are opposite in starting point when an obstacle exists on the curve; generating a random point on the map, and searching for a node of the extension tree nearest to the random point; judging whether an obstacle exists on a connecting line of the random point and the node; if yes, generating a random point again, and if not, taking a new node on the connecting line of the random point and the node; judging whether an obstacle exists on the connecting line between the nearest node and the new node; if yes, generating the random point again, otherwise, updating the node according to the obstacle safety distance constraint; afterwards, selecting a father node and a child node of the node according to the intelligent vehicle maximum steering constraint and the node; judging whether the distance between the two extension trees is smaller than a set threshold value or not, if being greater than the set threshold value, generating a random point again, otherwise, reversely selecting the path of the node to the starting point and the path of the node to the target point according to the nodes of the two extension trees to form an intelligent vehicle planned path.

Description

technical field [0001] The invention relates to the planning of the movement path of an object, in particular to a path planning method for an intelligent vehicle based on a bidirectionally extended random tree. Background technique [0002] In 2001, Kuffner and LaValle proposed a method of generating random trees in parallel (RRT-Connect) to improve the convergence speed of the algorithm. In 2010, Karaman and Frazzoli proposed the RRT* algorithm to solve the problem that the path generated by the RRT algorithm is not a probabilistic optimal solution, but its operating efficiency is low. In 2013, M.Jordan and A.Perez proposed B-RRT*, using the RRT* algorithm for bidirectional expansion of the starting point and the target point, in order to reduce the execution time of the RRT* algorithm, which is applied to mobile robots, B- Although RRT* can shorten part of the execution time, it still has the following disadvantages: [0003] (1) During the running process, global unifo...

AI Technical Summary

Problems solved by technology

[0003] (1) During the running process, global uniform random sampling is used, which increases the running time of the algorithm and slows down the convergence speed;

[0004] (2) Using the nearest node selection algorithm for node selection can easily lead to the failure of the planned path in complex scenarios;

[0005] (3) The planned path does not consider the vehicle kinematics constraints, so that the planned path cannot be used in the path planning of intelligent vehicles

Images