# Cooperative Coverage Control for Heterogeneous AUVs Based on Control Barrier Functions and Consensus Theory

## TL;DR

This paper introduces a control framework for coordinating a group of diverse underwater robots to efficiently cover an area while avoiding collisions and adhering to their physical limitations.

## Contribution

A novel hierarchical control framework combining consensus theory and control barrier functions for heterogeneous AUV swarms is proposed.

## Key findings

- A cooperative coverage model based on modified Voronoi partitions ensures workload balance among AUVs.
- Zeroing and High-Order Control Barrier Functions effectively manage safety constraints like collision avoidance and velocity limits.
- The proposed framework demonstrates stability, safety, and robustness in complex underwater simulations.

## Abstract

This paper addresses the problem of cooperative coverage control for heterogeneous Autonomous Underwater Vehicle (AUV) swarms operating in complex underwater environments. The objective is to achieve optimal coverage of a target region while simultaneously ensuring collision avoidance—both among AUVs and with static obstacles—and satisfying the inherent dynamic constraints of the AUVs. To this end, we propose a hierarchical control framework that fuses Control Barrier Functions (CBFs) with consensus theory. First, addressing the heterogeneity and limited sensing ranges of the AUVs, a cooperative coverage model based on a modified Voronoi partition is constructed. A nominal controller based on consensus theory is designed to balance the ratio of task workload to individual capability for each AUV. By minimizing a Lyapunov-like function via gradient descent, the swarm achieves self-organized optimal coverage. Second, to guarantee system safety, multiple safety constraints are designed for the AUV double-integrator dynamics, utilizing Zeroing Control Barrier Functions (ZCBFs) and High-Order Control Barrier Functions (HOCBFs). This approach unifies the handling of collision avoidance and velocity limitations. Finally, the nominal coverage controller and safety constraints are integrated into a Quadratic Programming (QP) formulation. This constitutes a safety-critical layer that modifies the control commands in a minimally invasive manner. Theoretical analysis demonstrates the stability of the framework, the forward invariance of the safe set, and the convergence of the coverage task. Simulation experiments verify the effectiveness and robustness of the proposed method in navigating obstacles and efficiently completing heterogeneous cooperative coverage tasks in complex environments.