Research on Path Planning Algorithm of Mobile Robot Based on RRT

Abstract

This research sheds light on the path planning algorithm for robots using the rapidly-exploring random tree (RRT). The RRT algorithm builds one path by generating random nodes in the robot's workspace, which links them to the nearest existing nodes, and expands the tree until the goal is attained or the highest amount of iterations is achieved. It can analyze the most feasible route, practical scenarios always tend towards randomness. The paper details the fundamental RRT calculation principles, specific steps for forming the RRT, and the tools used for implementation, including MATLAB, ROS, and Python. Experimental scenarios involved indoor environments with fixed obstacles to evaluate how the algorithm will perform. These results demonstrated that the RRT algorithm effectively guides robots through complex environments, though it has certain limitations such as instability in efficiency, high computational expense, and suboptimal path quality due to its random nature. The research also explores the algorithm's application across various fields, including robot motion planning, video games, autonomous exploration, and medical robotics. Despite its drawbacks, RRT's adaptability, simplicity, and potential of this research for integration with other algorithms make it accessible to make way for a great number of robot applications.