RGPB‐Planner: A Real‐Time Gaussian Potential B‐Spline Trajectory Planner for Unmanned Aerial Vehicles in Complex Environments

In this paper, a real‐time Gaussian potential B‐Spline planner (RGPB‐Planner) is proposed to address the problem of safe flight planning for unmanned aerial vehicles (UAVs) in complex environments. Existing trajectory optimization approaches, such as B‐Spline and safe‐corridor methods, can generate smooth trajectories. However, these approaches often suffer from high computational cost, local collisions near obstacles, or instability in dense environments. To fill this gap, the proposed RGPB‐Planner integrates a Gaussian potential with B‐Spline optimization, which ensures real‐time performance, smoothness, and safety by constraining control points within the safe potential region. The proposed RGPB‐Planner comprises two modules: the front‐end and back‐end modules. In the front‐end module, a local dynamic A‐star graph search is used to find the shortest path for UAVs to safely reach target points in complex environments. In the back‐end module, the front‐end shortest path is optimized using a Gaussian potential B‐Spline trajectory optimization method that considers the UAV's kinematics and dynamics constraints. Finally, simulation results demonstrate that the proposed RGPB‐Planner reduces flight time by 15%–19%, increases average velocity by 13%–21%, and shortens trajectory length by 2%–3% compared with Fast‐Planner and Ego‐Planner, while real‐world experiments validate its real‐time capability and flight safety. Therefore, the proposed RGPB‐Planner provides a reliable solution to ensure safe flight planning in complex environments.