Multi-AUVs cooperative path planning in 3D underwater terrain and vortex environments based on improved multi-objective particle swarm optimization algorithm

Abstract

This paper presents an improved multi-objective particle swarm optimization (IMOPSO) algorithm for cooperative path planning of multiple autonomous underwater vehicles (multi-AUVs) in 3D underwater terrain and vortex environment. Traditional PSO algorithms often struggle with local optima in high-dimensional, complex search spaces. To address this, we propose enhancements including Sine double-sequence perturbation interpolation for initial particle population generation, adaptive differential evolution strategies, iterative local search, and traction operations to enhance global search capability and avoid local optima. Additionally, cubic spline interpolation is employed to smooth the planned paths. The objective function is tailored to the ocean currents environment, considering path length, smoothness, deflection angle, and current impact, with a penalty function method to handle multi-AUVs cooperation and environmental constraints optimization for optimal energy consumption. Simulation results demonstrate that the proposed algorithm effectively plans short-range, high-security global paths for multi-AUVs, verifying its effectiveness and practicality in meeting the navigation requirements in ocean currents environments.