Trajectory Planning for UAV Transportation Systems Using RRT*-Informed NMPC

This paper presents a novel trajectory planning approach for two typical aerial transportation systems: UAV-slung-load and flying inverted pendulum. By integrating Rapidly-exploring Random Trees<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> (RRT*) into Nonlinear Model Predictive Control (NMPC), the proposed method en-hances motion planning, enabling effective navigation in complex environments while ensuring stability and safety. Simulation results demonstrate the approach's capability to overcome local minima and generate feasible trajectories, highlighting its potential to advance trajectory planning in UAV transportation systems.