Abstract
Autonomous landing of a quadrotor UAV on a vessel deck is challenging due to the special sea environment. In this paper, we present an on-board monocular vision based solution that provides a quadrotor with the capability to autonomously track and land on a vessel deck platform with simulated high sea state conditions. The whole landing process includes two stages, approaching from a long range and landing after hovering above the landing platform. Only on-board sensors are used in both stages, without external information input. We use Parrot AR.Drone as the experimental quadrotor platform, and a self-designed vessel deck emulator is constructed to evaluate the effectiveness of the proposed vessel deck landing solution. Experimental results demonstrate the accuracy and robustness of the developed landing algorithms.
Liyang Wang
Senior Supervisor R&D Engineer
My research interests include robotics, reinforcement learning, deep learning, path planning, motion planning, trajectory optimization, and controller design.