Landing a UAV in Harsh Winds and Turbulent Open Waters

TL;DR

This paper presents a novel model predictive control method enabling UAVs to autonomously land on USVs in harsh, turbulent waters using visual data, without communication, validated through simulations and real-world tests.

Contribution

A new MPC approach with a unique objective function and vessel motion decomposition for UAV landing on USVs in extreme conditions, relying solely on onboard visual data.

Findings

Successfully demonstrated in simulations under harsh conditions

Validated in real-world scenarios with robust performance

Achieved autonomous landing without USV communication

Abstract

Landing an unmanned aerial vehicle unmanned aerial vehicle (UAV) on top of an unmanned surface vehicle (USV) in harsh open waters is a challenging problem, owing to forces that can damage the UAV due to a severe roll and/or pitch angle of the USV during touchdown. To tackle this, we propose a novel model predictive control (MPC) approach enabling a UAV to land autonomously on a USV in these harsh conditions. The MPC employs a novel objective function and an online decomposition of the oscillatory motion of the vessel to predict, attempt, and accomplish the landing during near-zero tilt of the landing platform. The nonlinear prediction of the motion of the vessel is performed using visual data from an onboard camera. Therefore, the system does not require any communication with the USV or a control station. The proposed method was analyzed in numerous robotics simulations in harsh and extreme conditions and further validated in various real-world scenarios.