DP-NAV: A Hybrid Algorithm for Dynamic Path Planning in Autonomous Underwater Vehicles
Matthew Rice, Sayani Sarkar, Nathan Johnson · 2025
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.