Autonomous Underwater Vehicle Navigation Based on iWOA-UFastSLAM

Unscented FastSLAM (UFastSLAM) is a crucial method for achieving fully autonomous navigation in Autonomous Underwater Vehicles (AUVs). To address the problem of decreased navigation accuracy caused by particle degeneracy and particle impoverishment in traditional UFastSLAM, an UFastSLAM based on improved Whale Optimization Algorithm (iWOA-UFastSLAM) is proposed. The algorithm adjusts the proposal distribution of particles in UFastSLAM, bringing the particle distribution closer to the true pose state of the AUV and mitigating the problem of particle degeneracy. To enhance the convergence of the algorithm, an inertia weight factor is introduced in the Whale Optimization Algorithm’s position update formula to improve global search and local optimization capabilities. Additionally, the Cauchy distribution is utilized to perturb the optimal whale position, increasing population diversity and alleviating the issue of particle depletion. Simulation experiments demonstrate that the proposed algorithm significantly improves the estimation accuracy of AUV autonomous navigation and reduces the algorithm’s complexity when compared to the original UFastSLAM algorithm. Moreover, the proposed algorithm has important implications for the practical path tracking of AUVs.