State estimation of marine vessels affected by waves by unmanned aerial vehicles

TL;DR

This work tackles robust state estimation of a USV moving on rough water by a cooperating UAV. It introduces a novel nonlinear 6-DOF USV model that explicitly incorporates wave dynamics and fuses data from UAV and USV sensors through an Unscented Kalman Filter to estimate and predict all six pose and velocity states. A linear variant and a comprehensive verification framework (innovation tests and RMSE comparisons) are developed, with extensive Gazebo simulations and real-world experiments showing improved accuracy over state-of-the-art methods. The results enable reliable cooperative landing and manipulation tasks under varying sea conditions, contributing to safer and more capable UAV–USV operations.

Abstract

A novel approach for robust state estimation of marine vessels in rough water is proposed in this paper to enable tight collaboration between Unmanned Aerial Vehicles (UAVs) and a marine vessel, such as cooperative landing or object manipulation, regardless of weather conditions. Our study of marine vessel (in our case Unmanned Surface Vehicle (USV)) dynamics influenced by strong wave motion has resulted in a novel nonlinear mathematical USV model with 6 degrees of freedom (DOFs), which is required for precise USV state estimation and motion prediction. The proposed state estimation and prediction approach fuses data from multiple sensors onboard the UAV and the USV to enable redundancy and robustness under varying weather conditions of real-world applications. The proposed approach provides estimated states of the USV with 6 DOFs and predicts its future states to enable tight control of both vehicles on a receding control horizon. The proposed approach was extensively tested in the realistic Gazebo simulator and successfully experimentally validated in many real-world experiments representing different application scenarios, including agile landing on an oscillating and moving USV. A comparative study indicates that the proposed approach significantly surpassed the current state-of-the-art.

Figures (16)

• Figure 1: UAV used in the presented research cooperating together with a USV equipped with a landing platform.
• Figure 2: Unsuccessful landing on a marine vessel without the landing pad motion detection and prediction (mainly the heave motion was difficult to observe), shown in (a) and (b), serves as motivation to create a robust USV state estimation system in 6 DOFs to ensure safe autonomous landing in rough water and different real-world conditions as shown in snapshots from our system deployment (c) and (d).
• Figure 3: Motion of the USV with 6 DOFs.
• Figure 4: The GPS and the IMU placed onboard the USV.
• Figure 5: UV LEDs (marked with red circles) placed on the USV board (a) together with detected UV LEDs in the UV camera image (b).