Deep-Sea A*+: An Advanced Path Planning Method Integrating Enhanced A* and Dynamic Window Approach for Autonomous Underwater Vehicles
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917812543422464 |
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| author | Lai, Yinyi Shang, Jiaqi Liu, Zenghui Jiang, Zheyu Li, Yuyang Chen, Longchao |
| author_facet | Lai, Yinyi Shang, Jiaqi Liu, Zenghui Jiang, Zheyu Li, Yuyang Chen, Longchao |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_16762 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep-Sea A*+: An Advanced Path Planning Method Integrating Enhanced A* and Dynamic Window Approach for Autonomous Underwater Vehicles Lai, Yinyi Shang, Jiaqi Liu, Zenghui Jiang, Zheyu Li, Yuyang Chen, Longchao Robotics Artificial Intelligence 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. |
| title | Deep-Sea A*+: An Advanced Path Planning Method Integrating Enhanced A* and Dynamic Window Approach for Autonomous Underwater Vehicles |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2410.16762 |