Local Trajectory Planning For UAV Autonomous Landing
This addresses real-time local trajectory planning for UAVs in urban environments, but it is incremental as it builds on existing methods with new evaluation in simulated scenarios.
The paper tackles the problem of autonomous UAV landing with obstacle avoidance by proposing a novel optimization framework that uses a pre-planned global path and priority map, showing it improves landing-mission success probability in real-time simulations.
An important capability of autonomous Unmanned Aerial Vehicles (UAVs) is autonomous landing while avoiding collision with obstacles in the process. Such capability requires real-time local trajectory planning. Although trajectory-planning methods have been introduced for cases such as emergency landing, they have not been evaluated in real-life scenarios where only the surface of obstacles can be sensed and detected. We propose a novel optimization framework using a pre-planned global path and a priority map of the landing area. Several trajectory planning algorithms were implemented and evaluated in a simulator that includes a 3D urban environment, LiDAR-based obstacle-surface sensing and UAV guidance and dynamics. We show that using our proposed optimization criterion can successfully improve the landing-mission success probability while avoiding collisions with obstacles in real-time.