Collaborative Object Manipulation on the Water Surface by a UAV-USV Team Using Tethers

TL;DR

This work tackles object manipulation on water by leveraging a tethered collaboration between a UAV and a USV, addressing limitations of single-robot approaches. It introduces a coupled dynamic model for the UAV-USV-object system and embeds it into a Model Predictive Control framework, with discretization via RK4 and VP-frame linearization to enable tractable optimization. The approach is validated in Gazebo and VRX simulations, showing a 40% reduction in mean tracking error and a fourfold faster disturbance recovery compared to a single-robot baseline, along with robust performance on circular trajectories and under external disturbances. The results suggest that the UAV-USV tethered team can extend mission reliability and effectiveness for water-surface manipulation tasks, with practical implications for sensing, debris collection, and environmental monitoring.

Abstract

This paper introduces an innovative methodology for object manipulation on the surface of water through the collaboration of an Unmanned Aerial Vehicle (UAV) and an Unmanned Surface Vehicle (USV) connected to the object by tethers. We propose a novel mathematical model of a robotic system that combines the UAV, USV, and the tethered floating object. A novel Model Predictive Control (MPC) framework is designed for using this model to achieve precise control and guidance for this collaborative robotic system. Extensive simulations in the realistic robotic simulator Gazebo demonstrate the system's readiness for real-world deployment, highlighting its versatility and effectiveness. Our multi-robot system overcomes the state-of-the-art single-robot approach, exhibiting smaller control errors during the tracking of the floating object's reference. Additionally, our multi-robot system demonstrates a shorter recovery time from a disturbance compared to the single-robot approach.

Figures (9)

• Figure 1: The multi-robot system consisting of a USV, UAV, and a solid object representing a sensor, garbage, or arbitrary floating object. This object is simultaneously tethered to the USV and UAV.
• Figure 2: The depiction of the world frame $\mathcal{W} = \{\mathbf{w}_{x}, \mathbf{w}_{y}, \mathbf{w}_{z}\}$ in which the 3D position and orientation of the UAV $\mathcal{B}_{u} = \{\mathbf{b}_{u,x}, \mathbf{b}_{u,y}, \mathbf{b}_{u,z}\}$, USV $\mathcal{B}_{b} = \{\mathbf{b}_{b,x}, \mathbf{b}_{b,y}, \mathbf{b}_{b,z}\}$ and floating object $\mathcal{B}_{o} = \{\mathbf{b}_{o,x}, \mathbf{b}_{o,y}, \mathbf{b}_{o,z}\}$ body frames are expressed. The red vectors $\bm{F}_{u}$ and $\bm{F}_{b}$ show the forces applied to the floating object by the UAV and USV tethers. The Vessel parallel coordinate system $\mathcal{VP}=\{ \mathbf{vp}_{x},\mathbf{vp}_{y},\mathbf{vp}_{z}\}$ is depicted in blue and is parallel to the USV body frame $\mathcal{B}_{b}$.
• Figure 3: Pipeline diagram of the proposed collaborative multi-robot control approach presented in this paper containing the MRS system baca2021MRSUAVSystem for verification in realistic robotic scenarios. Mission & navigation block decides about the reference of the floating object based on the current states and mission task. Then, Multi-robots system controller computes the desired trajectory references using the MPC for the UAV and USV. The computed trajectory references are forwarded into the Reference controller blocks of the UAV and USV. The UAV Reference controller creates the desired thrust and angular velocities $(\bm{\omega}_d,T_d)$ for the Pixhawk embedded flight controller that commands the UAV actuators. The UAV State estimator fuses data from the Odometry & localization block to estimate the UAV translation, rotation, and angular velocities $(\mathbf{x},\mathbf{R},\bm{\omega})$. The USV Reference controller creates the desired thrust $\bm{\tau}_b$ for USV actuators. The USV State estimator fuses data from the Odometry & localization block to estimate the USV states $(\bm{\eta}_b,\bm{\nu}_b)$. The floating object State estimator fuses data from its Onboard sensors to estimate states of the object $(\bm{\bm{\eta}}_o,\bm{\nu}_o)$.
• Figure 4: Our multi-robot system, comprising the UAV, USV, and floating object, is depicted in the Gazebo simulator and RViz during its mission.
• Figure 5: The results of the experiment, where our multi-robot system tracks a circular trajectory for the object. The graphs illustrate the position of the multi-robot system and the reference in a horizontal plane, as well as the distance of the object to the reference and the velocities of the UAV, USV, and the object.