DP-NAV: A Hybrid Algorithm for Dynamic Path Planning in Autonomous Underwater Vehicles

Autonomous Underwater Vehicles (AUVs) face significant challenges in underwater navigation, including generating smooth paths, avoiding obstacles, and adapting to complex conditions. This paper introduces a hybrid path-planning algorithm, DP-Nav, that integrates the D* Lite algorithm for efficient initial pathfinding with Proximal Policy Optimization (PPO) to refine paths for smoother trajectories. The proposed approach addresses D* Lite's inability to produce continuous, smooth paths and PPO's failures in environments requiring significant detours. Experimental results in four progressively complex environments highlight DP-Nav's 100% task completion rate compared to PPO's failure to converge in 50% of the cases. DP-Nav achieved smoother paths, with a path length improvement of up to 5% over D* Lite in critical scenarios, while maintaining competitive planning times. These findings underscore the DP-Nav's ability to deliver optimal and reliable navigation, making it well-suited for applications such as environmental monitoring and disaster response. Future work will extend testing to dynamic environments, three-dimensional navigation, and kinematic constraints to further enhance its operational feasibility.