Motion Planning of Unmanned Aerial Vehicle Based on Rapid-exploration Random Tree Algorithm

Abstract

In the study of route planning problems in complex environments, in order to reduce the flight cost of unmanned aerial vehicles (UAVs), it is necessary to achieve a better balance between planning time and path quality. This paper utilizes the Rapid-exploration Random Tree (RRT) algorithm for motion planning of a fixed-wing UAV and a multi-rotor UAV (i.e., a quad-rotor UAV), and gives the origin and destination locations on a 3-D map. By following aerodynamic constraints such as maximum roll angle, flight path angle, and airspeed, a collision-free and flight-friendly path is found through simulation. In addition, this paper employs a path smoothing algorithm to simplify the 3-D Dubins path and generates the shortest trajectory. The simulation results show that the RRT and the optimization method have faster convergence speed and shorter search time, reduce redundant planning points, shorten the planning track, and improve the track planning efficiency.

Enhancing Path Planning Efficiency for Underwater Gravity Matching Navigation with a Novel Three-Dimensional Along-Path Obstacle Profiling Algorithm

“…To improve the search efficiency of the RRT* algorithm, the Q-RRT* algorithm extends the range of parent node selection for new nodes and nearby nodes based on the triangle inequality, which can generate better initial paths and achieve a faster convergence speed [26,27]. RRT-based algorithms exhibit significantly reduced search efficiency when dealing with high-dimensional spaces and complex obstacles, leading to a substantial increase in the required number of iterations and computation time [28][29][30][31][32]. The Depth Sorting Fast Search (DSFS) algorithm enhances the efficiency of the parent node reselection process and rewiring process by utilizing the inequality relationship between ancestor nodes and their descendants.…”