PierGuard: A Planning Framework for Underwater Robotic Inspection of Coastal Piers
Using underwater robots instead of humans for the inspection of coastal piers can enhance efficiency while reducing risks. A key challenge in performing these tasks lies in achieving efficient and rapid path planning within complex environments. Sampling-based path planning methods, such as Rapidly-...
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| Vydáno v: | IEEE transactions on automation science and engineering Ročník 22; s. 15941 - 15952 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
2025
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| Témata: | |
| ISSN: | 1545-5955, 1558-3783 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Using underwater robots instead of humans for the inspection of coastal piers can enhance efficiency while reducing risks. A key challenge in performing these tasks lies in achieving efficient and rapid path planning within complex environments. Sampling-based path planning methods, such as Rapidly-exploring Random Tree* (RRT*), have demonstrated notable performance in high-dimensional spaces. In recent years, researchers have begun designing various geometry-inspired heuristics and neural network-driven heuristics to further enhance the effectiveness of RRT*. However, the performance of these general path planning methods still requires improvement when applied to highly cluttered underwater environments. In this paper, we propose PierGuard, which combines the strengths of bidirectional search and neural network-driven heuristic regions. We design a specialized neural network to generate high-quality heuristic regions in cluttered maps, thereby improving the performance of the path planning. Through extensive simulation and real-world ocean field experiments, we demonstrate the effectiveness and efficiency of our proposed method compared with previous research. Our method achieves approximately 2.6 times the performance of the state-of-the-art geometric-based sampling method and nearly 4.9 times that of the state-of-the-art learning-based sampling method. Our results provide valuable insights for the automation of pier inspection and the enhancement of maritime safety ( https://youtu.be/A54AJ4bbX98 ). Note to Practitioners-The research presented in this paper focuses on enhancing the efficiency and safety of underwater inspections of coastal piers by utilizing underwater robots instead of human divers. One of the primary challenges in this context is the development of efficient path planning strategies within complex, dynamic environments. Our study investigates the use of sampling-based path planning methods, specifically RRT*, to improve the efficiency of these inspections. The key takeaway for practitioners is that integrating bidirectional search with neural heuristic regions in the path planning of underwater robots can significantly enhance inspection efficiency, reducing both the time required and the complexity of navigating hazardous environments. The method is particularly useful in environments where the terrain is difficult to model or constantly changing. By adopting these algorithms, practitioners can improve operational efficiency, enhance safety protocols, and reduce the overall cost of inspections. We recommend that practitioners looking to implement this approach invest in suitable robotic platforms capable of handling the complexities of underwater navigation. Additionally, integrating these path planning methods with real-time data acquisition systems will further enhance the effectiveness of inspections, allowing for more accurate and faster assessments of coastal infrastructure. |
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| ISSN: | 1545-5955 1558-3783 |
| DOI: | 10.1109/TASE.2025.3570694 |