Path Planning for Autonomous Underwater Vehicles in Complex Environments Based on a PRM* Algorithm
Ali Arifi, Raja Jarray, Soufiene Bouallègue · 2025
This paper proposes a Probabilistic Roadmap (PRM) planner for Autonomous Underwater Vehicles (AUVs) navigating in complex 3D environments. An improved version of PRM algorithms, called PRM*, is introduced to improve the capabilities of nodes specification, edges construction, and graph creation. For implementation, a pseudo-code for the proposed PRM* planner is provided. Numerical simulations are performed in different scenarios with increasing number of static obstacles and complexity. Evaluations of the generated collision-free paths are carried out using the Straight-Line Rate (SLR) and Computational Time (CT) metrics. Analyses and comparisons are performed to validate the significance and superiority of the proposed PRM* planner. Demonstrative results show that the PRM* algorithm outperforms the ordinary PRM one in terms of path compactness, smoothness, and collision avoidance.