An experimental vision-based integrated guidance and control strategy for autonomous landing of a faulty UAV

Abstract

Uncrewed Aerial Vehicles (UAVs) have emerged as a transformative asset in surveillance, mapping, and delivery tasks since they have sophisticated autonomous capabilities. This paper develops a practical vision-based optimal flight planning strategy for autonomous safe landing and control of a multirotor UAV using a low-cost monocular camera in the presence of a motor fault. An optimal integrated guidance and control strategy is developed by utilizing an innovative discrete system model and a state observer from the triggering point to the identified landing position. Additionally, compatible image processing techniques and UAV kinematics are integrated to detect the suitable landing site and translate its location into desired attitude inputs to the controller. This approach empowers the UAV to autonomously land in no-GPS environments, relying solely on camera data. Initially, vision-based sensors, image processing techniques, and the developed guidance and control algorithms undergo initial evaluation in Software in the Loop (SIL) simulations using the Robot Operating System (ROS) and Gazebo simulation environments. The efficacy of the proposed framework is then assessed through experimental flight tests across various landing scenarios, accounting for local wind conditions and motor faults.