Towards UAV-USV Collaboration in Harsh Maritime Conditions Including Large Waves

TL;DR

This work tackles the challenge of coordinating UAVs with USVs in harsh maritime environments featuring large waves, focusing on following and deck-landing tasks. It introduces a novel linear $6$-DOF USV model with integrated wave dynamics, coupled with a state estimator and predictor that fuse data from onboard UAV and USV sensors, feeding a model-predictive control trajectory planner to generate UAV trajectories. The approach is validated through extensive Gazebo/VRX simulations and real-world experiments, showing improved estimation accuracy—especially in orientation—compared to a state-of-the-art baseline and enabling reliable landings on a moving USV. The results demonstrate the practicality of wave-aware UAV–USV collaboration, with potential applications in tethered power delivery, garbage removal, and water-quality monitoring in challenging sea states.

Abstract

This paper introduces a system designed for tight collaboration between Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) in harsh maritime conditions characterized by large waves. This onboard UAV system aims to enhance collaboration with USVs for following and landing tasks under such challenging conditions. The main contribution of our system is the novel mathematical USV model, describing the movement of the USV in 6 degrees of freedom on a wavy water surface, which is used to estimate and predict USV states. The estimator fuses data from multiple global and onboard sensors, ensuring accurate USV state estimation. The predictor computes future USV states using the novel mathematical USV model and the last estimated states. The estimated and predicted USV states are forwarded into a trajectory planner that generates a UAV trajectory for following the USV or landing on its deck, even in harsh environmental conditions. The proposed approach was verified in numerous simulations and deployed to the real world, where the UAV was able to follow the USV and land on its deck repeatedly.

Figures (8)

• Figure 1: The tight collaboration between UAVs (marked with white circles) and USVs during the landing and following tasks using our system presented in this paper.
• Figure 2: The depiction of the world frame $\mathcal{W} = \{\mathbf{w}_{x}, \mathbf{w}_{y}, \mathbf{w}_{z}\}$, Vessel parallel coordinate system $\mathcal{VP}=\{ \mathbf{vp}_{x},\mathbf{vp}_{y},\mathbf{vp}_{z}\}$, and USV body-fixed coordinate frame $\mathcal{B}_{b} = \{\mathbf{b}_{b,x}, \mathbf{b}_{b,y}, \mathbf{b}_{b,z}\}$.
• Figure 3: The figure depicts a pipeline diagram of the entire system used for experimental verification in this paper. The State estimator fuses data from USV onboard sensors (GPS and IMU) and UAV onboard sensors (AprilTag detector and UVDAR system). The estimated USV states are then sent to the State predictor, which predicts future USV states. The Trajectory planner uses the estimated and predicted USV states to generate a UAV trajectory, which is precisely tracked by the MRS UAV systembaca2021mrs.
• Figure 4: The USV board showing an AprilTag, UV LED (marked with red circles), and MRS boat unit, which contains GPS and IMU.
• Figure 5: Estimated USV position $\bm{p}=(x, y, z)$ and orientation $\bm{\Theta}=(\phi, \theta, \psi)$ using the method proposed in this paper.