Three-Dimensional Path Planning Algorithm for Global Path of AUV Based on ROBL-MFO on Static Environment

Abstract

AbstractIn this research we study the optimization problem of path planning in an oceanic ecosystem with a Static current in the presences of various obstacles with the autonomy of Autonomous underwater vehicles (AUV). We introduce an improved Moth flame optimization algorithm called refraction principle and opposite-based-learning Moth flame optimization (ROBL-MFO) in a Static current environment. This algorithm addresses the differences between the traditional Moth flame optimization (MFO) algorithm. First, it adjusts the moth position update formula we deal with the term of the historically optimal flame average, and improves the convergence velocity of the algorithm. Secondly, it uses a random inverse learning strategy to narrow down the algorithm search. Finally, the principle of refraction provides the algorithm with the ability to jump from the local optima and help the algorithm avoid premature convergence. The numerical results show that the ROBL-MFO improves the classical MFO, in terms of optimization criteria.KeywordsAutonomous underwater vehiclesPath planningStatic currentRefraction principle and opposite-based learning - Moth flame optimization