Obstacle avoidance path planning based on RRT algorithm

Obstacle avoidance path planning based on RRT algorithm

Path planning is one of the important issues in the field of robotics. Among them, obstacle avoidance path planning refers to finding a safe and effective path for the robot to avoid obstacles and reach the target location. The Rapidly-Exploring Random Trees (RRT) algorithm is a commonly used obstacle avoidance path planning algorithm. It is based on the ideas of random sampling and tree structure and can efficiently search for feasible paths. This article will introduce obstacle avoidance path planning based on the RRT algorithm and provide corresponding MATLAB code examples.

The basic principle of the RRT algorithm is to search for feasible paths by continuously randomly sampling and expanding the nodes of the tree. The core steps of the algorithm include: initializing the tree, random sampling, nearest neighbor node search, node expansion and connection, etc.

First, we need to initialize the tree. In MATLAB code, we can define a tree structure containing a root node and a set of child nodes. The root node represents the initial position of the robot, while the child nodes represent the branches of the tree.

classdef Node
    properties
        Point % 节点的坐标
        Parent % 父节点
    end
    methods
        function node = Node(point, parent)
            node.