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

Area of Science:

• Robotics and Control Systems
• Multi-Agent Systems
• Autonomous Navigation

Background:

• Cooperative coverage control is crucial for autonomous underwater vehicle (AUV) swarms in complex environments.
• Heterogeneity in AUV capabilities and sensing ranges presents significant challenges.
• Ensuring collision avoidance and dynamic constraints alongside coverage is a key problem.

Purpose of the Study:

• To develop a hierarchical control framework for heterogeneous AUV swarms.
• To achieve optimal area coverage while guaranteeing collision avoidance and respecting AUV dynamics.
• To enhance the robustness and efficiency of cooperative underwater missions.

Main Methods:

• A modified Voronoi partition for cooperative coverage modeling.
• Consensus theory for workload balancing and self-organized coverage.
• Control Barrier Functions (CBFs), including Zeroing CBFs (ZCBFs) and High-Order CBFs (HOCBFs), for safety guarantees.
• Quadratic Programming (QP) to integrate coverage control with safety constraints.

Main Results:

• The proposed framework ensures stability and forward invariance of the safe set.
• Demonstrated convergence of the cooperative coverage task.
• Successfully unified collision avoidance and velocity limitations for AUVs.
• Validated effectiveness and robustness in complex environments with obstacles.

Conclusions:

• The hierarchical control framework effectively manages heterogeneous AUV swarms for optimal coverage and safety.
• The integration of CBFs and consensus theory provides a robust solution for complex underwater missions.
• The method offers a minimally invasive safety-critical layer for autonomous systems.