Fast Dynamic P-RRT*-Based UAV Path Planning and Trajectory Tracking Control Under Dense Obstacles

Abstract

This work develops an improved integrated planning and control framework for an unmanned aerial vehicle (UAV) in complex environments with dense obstacles to achieve fast and accurate path planning, trajectory generation, and tracking control. Utilizing the potential function-based rapid-exploration random tree star (P-RRT*), a bidirectional dynamic informed P-RRT* (BDIP-RRT*) algorithm is first introduced to enhance sampling efficiency, facilitating swift path generation. To further optimize the initial path, a greedy algorithm is employed to minimize redundant segments within the generated path. Subsequently, trajectory control points are assigned based on the original path points using an adaptive distance interpolation strategy. A hybrid optimized trajectory generator considering jerk and snap is built to obtain a reference trajectory for the UAV. Moreover, two prescribed-time control laws are designed to ensure fast and accurate UAV position and attitude control. Finally, simulation results are performed to illustrate the effectiveness and superior performances of the developed path planning and control scheme.