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

• URL: http://arxiv.org/abs/2410.16762v1
• Date: Tue, 22 Oct 2024 07:29:05 GMT
• Title: Deep-Sea A*+: An Advanced Path Planning Method Integrating Enhanced A* and Dynamic Window Approach for Autonomous Underwater Vehicles
• Authors: Yinyi Lai, Jiaqi Shang, Zenghui Liu, Zheyu Jiang, Yuyang Li, Longchao Chen,
• Abstract summary: Extreme conditions in the deep-sea environment pose significant challenges for underwater operations. We propose an advanced path planning methodology that integrates an improved A* algorithm with the Dynamic Window Approach (DWA) Our proposed method surpasses the traditional A* algorithm in terms of path smoothness, obstacle avoidance, and real-time performance.
• Score: 1.3807821497779342
• License:
• 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.

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