Local Trajectory Planning For UAV Autonomous Landing

TL;DR

This paper presents a real-time local trajectory planning method for UAV autonomous landing that improves success probability and obstacle avoidance using a novel optimization framework in urban environments.

Contribution

It introduces a new optimization framework combining global paths and priority maps, evaluated in realistic simulations for UAV landing.

Findings

Improved landing success rate in simulated urban environments.

Effective obstacle avoidance using surface sensing with LiDAR.

Real-time trajectory planning enhances safety and reliability.

Abstract

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.