Distributed Motion Control for Multiple Connected Surface Vessels

TL;DR

This paper introduces a scalable cooperative control method for multiple connected surface vessels, enabling trajectory tracking as a single floating structure using leader-follower dynamics, NMPC, and onboard sensors.

Contribution

It presents a novel decentralized control approach that coordinates connected vessels without explicit communication, utilizing nonlinear model predictive control and consensus algorithms.

Findings

Successful hardware experiments with three Roboat vessels demonstrate effective trajectory tracking.

Simulation results with up to 65 robots confirm the scalability of the control approach.

The method enables coordinated movement of connected vessels in water environments.

Abstract

We propose a scalable cooperative control approach which coordinates a group of rigidly connected autonomous surface vessels to track desired trajectories in a planar water environment as a single floating modular structure. Our approach leverages the implicit information of the structure's motion for force and torque allocation without explicit communication among the robots. In our system, a leader robot steers the entire group by adjusting its force and torque according to the structure's deviation from the desired trajectory, while follower robots run distributed consensus-based controllers to match their inputs to amplify the leader's intent using only onboard sensors as feedback. To cope with the complex and highly coupled system dynamics in the water, the leader robot employs a nonlinear model predictive controller (NMPC), where we experimentally estimated the dynamics model of the floating modular structure in order to achieve superior performance for leader-following control. Our method has a wide range of potential applications in transporting humans and goods in many of today's existing waterways. We conducted trajectory and orientation tracking experiments in hardware with three custom-built autonomous modular robotic boats, called Roboat, which are capable of holonomic motions and onboard state estimation. Simulation results with up to 65 robots also prove the scalability of our proposed approach.