# A Safe Maritime Path Planning Fusion Algorithm for USVs Based on Reinforcement Learning A* and LSTM-Enhanced DWA

## TL;DR

This paper introduces a new hybrid algorithm for safe and smooth path planning for unmanned surface vehicles in complex maritime environments.

## Contribution

The novel approach combines reinforcement learning-enhanced A* with LSTM and Kalman Filter for improved obstacle prediction and smoother trajectories.

## Key findings

- The enhanced A* algorithm produces shorter and smoother paths for USVs.
- The LSTM-Kalman Filter combination improves dynamic obstacle prediction accuracy.
- The improved DWA reduces collision risks and ensures compliance with maritime navigation rules.

## Abstract

In complex maritime environments, the safety of path planning for Unmanned Surface Vehicles (USVs) remains a significant challenge. Existing methods for handling dynamic obstacles often suffer from inadequate predictability and generate non-smooth trajectories. To address these issues, this paper proposes a reliable hybrid path planning approach that integrates a reinforcement learning-enhanced A* algorithm with an improved Dynamic Window Approach (DWA). Specifically, the A* algorithm is augmented by incorporating a dynamic five-neighborhood search mechanism, a reinforcement learning-based adaptive weighting strategy, and a path post-optimization procedure. These enhancements collectively shorten the path length and significantly improve trajectory smoothness. While ensuring that the global path avoids dynamic obstacles smoothly, a Kalman Filter (KF) is integrated into the Long Short-Term Memory (LSTM) network to preprocess historical data. This mechanism suppresses transient outliers and stabilizes the trajectory prediction of dynamic obstacles. Moreover, the evaluation function of the DWA is refined by incorporating the International Regulations for Preventing Collisions at Sea (COLREGs) constraints, enabling compliant navigation behaviors. Simulation results in MATLAB demonstrate that the enhanced A* algorithm better conforms to the kinematic model of the USVs. The improved DWA significantly reduces collision risks, thereby ensuring safer navigation in dynamic marine environments.