Receding horizon–based RRT* algorithm for a UAV real-time path planner

Abstract

This paper describes the real-time application of the rapidly exploring random tree* (RRT*) algorithm, which is a type of sample-based path planning. The RRT* algorithm can generate an optimized path depending on the number of nodes. However, as the number of nodes increases, the computational speed slows down because of the scanning procedure to find the best nodes, which is why the RRT* algorithm is not normally suitable for real-time applications. Many nodes need to be considered to optimize a path through the entire workspace. If the optimization process can be performed sequentially in the receding horizon area, the computational load can be reduced because fewer nodes are considered. This paper presents the receding horizon–based RRT* (RH-RRT*) algorithm, which uses a biased random sample and node removal method to solve this problem. The algorithm was then applied to a quadrotor simulation as a demonstration.