Three-Dimensional Coverage Path Planning for Cooperative Autonomous Underwater Vehicles: A Swarm Migration Genetic Algorithm Approach

Abstract

Cooperative marine exploration tasks involving multiple autonomous underwater vehicles (AUVs) present a complex 3D coverage path planning challenge that has not been fully addressed. To tackle this, we employ an auto-growth strategy to generate interconnected paths, ensuring simultaneous satisfaction of the obstacle avoidance and space coverage requirements. Our approach introduces a novel genetic algorithm designed to achieve equivalent and energy-efficient path allocation among AUVs. The core idea involves defining competing gene swarms to facilitate path migration, corresponding to path allocation actions among AUVs. The fitness function incorporates models for both energy consumption and optimal path connections, resulting in iterations that lead to optimal path assignment among AUVs. This framework for multi-AUV coverage path planning eliminates the need for pre-division of the working space and has proven effective in 3D underwater environments. Numerous experiments validate the proposed method, showcasing its comprehensive advantages in achieving equitable path allocation, minimizing overall energy consumption, and ensuring high computational efficiency. These benefits contribute to the success of multi-AUV cooperation in deep-sea information collection and environmental surveillance.