Path planning for autonomous underwater vehicles requires reliable and computationally efficient methods, particularly in cluttered environments. This work presents a comparative evaluation of representative approaches, including metaheuristic optimization methods (continuous genetic algorithm, particle swarm optimization, gray wolf optimizer, and Jaya), a sampling-based method (probabilistic roadmap with genetic refinement), a reactive strategy (artificial potential fields), and a control-based approach (model predictive control with control barrier functions). The algorithms are assessed in a controlled two-dimensional simulated workspace with randomly generated obstacles and systematically increasing obstacle density. Each configuration is evaluated across multiple independent trials using metrics such as success rate, path length, and convergence behavior. The effect of environmental disturbances is examined by analyzing particle swarm optimization under Gauss–Markov current models. The results show that performance depends strongly on the ability to preserve feasibility as obstacle density increases. The probabilistic roadmap with genetic refinement demonstrated the highest robustness, maintaining feasibility across all scenarios, while particle swarm optimization provided a strong balance between path quality and reliability in low-to-moderate clutter. The introduction of current disturbances led to reduced efficiency and consistency. Statistical analysis confirmed significant differences among methods, highlighting that rank-based superiority does not necessarily reflect practical robustness in constrained environments.
Castro et al. (Thu,) studied this question.