Deep-Sea A*+: An Advanced Path Planning Method Integrating Enhanced A* and Dynamic Window Approach for Autonomous Underwater Vehicles

TL;DR

This paper introduces a novel path planning method for autonomous underwater vehicles that combines an improved A* algorithm with the Dynamic Window Approach, enhancing efficiency, smoothness, and obstacle avoidance in complex deep-sea environments.

Contribution

It presents an integrated path planning framework that optimizes A* search and incorporates real-time obstacle avoidance for AUVs, advancing underwater navigation capabilities.

Findings

Outperforms traditional A* in path smoothness and obstacle avoidance

Reduces computational load and accelerates path search

Enhances real-time response in dynamic environments

Abstract

As terrestrial resources become increasingly depleted, the demand for deep-sea resource exploration has intensified. However, the extreme conditions in the deep-sea environment pose significant challenges for underwater operations, necessitating the development of robust detection robots. In this paper, we propose an advanced path planning methodology that integrates an improved A* algorithm with the Dynamic Window Approach (DWA). By optimizing the search direction of the traditional A* algorithm and introducing an enhanced evaluation function, our improved A* algorithm accelerates path searching and reduces computational load. Additionally, the path-smoothing process has been refined to improve continuity and smoothness, minimizing sharp turns. This method also integrates global path planning with local dynamic obstacle avoidance via DWA, improving the real-time response of underwater robots in dynamic environments. Simulation results demonstrate that our proposed method surpasses the traditional A* algorithm in terms of path smoothness, obstacle avoidance, and real-time performance. The robustness of this approach in complex environments with both static and dynamic obstacles highlights its potential in autonomous underwater vehicle (AUV) navigation and obstacle avoidance.