An enhanced A* method incorporating an encrypted memory database for ASV efficient local path planning
Gespeichert in:
| Titel: | An enhanced A* method incorporating an encrypted memory database for ASV efficient local path planning |
|---|---|
| Autoren: | Yuanshuo Liu, Defeng Wu, Zheng You |
| Quelle: | Mathematical Biosciences and Engineering, Vol 21, Iss 2, Pp 2302-2322 (2024) |
| Verlagsinformationen: | American Institute of Mathematical Sciences (AIMS), 2024. |
| Publikationsjahr: | 2024 |
| Schlagwörter: | 0209 industrial biotechnology, memory database, improved heuristic function, QA1-939, 0211 other engineering and technologies, autonomous surface vehicle (asv), 02 engineering and technology, enhanced a* algorithm, path planning, TP248.13-248.65, Mathematics, Biotechnology |
| Beschreibung: | For the autonomous surface vehicle (ASV) planning problem, an enhanced A* method incorporating encrypted memory database for ASV efficient local path planning is proposed. Considering the current various path planning problems mostly use methods with high time complexity, such as neural networks, we select the A* algorithm with low time complexity as the basis. To speed up the path planning rate and further improve the real-time and realistic algorithm, this paper modifies the heuristic function of the A* algorithm by combining the motion mode of ASV. In response to the problem that the target point is far from the detection, we improve the target point design mechanism and create a new temporary target point within the detection range. In addition, the algorithm incorporates a memory database, which can record commonly used waters or retain the environmental path of navigated waters as a priori information. When the same waters are reencountered, the memory database information can be read directly to complete the navigation. Moreover, the memory database is encrypted to prevent information leakage. Finally, a simulation environment is built to verify the effectiveness of the proposed algorithm by comparison with some existing algorithms. |
| Publikationsart: | Article |
| ISSN: | 1551-0018 |
| DOI: | 10.3934/mbe.2024101 |
| Zugangs-URL: | https://pubmed.ncbi.nlm.nih.gov/38454684 https://doaj.org/article/cb2c6d0da113437680569466f36fbd0a |
| Dokumentencode: | edsair.doi.dedup.....c64fb521293a64a9bf9250ea46bc82c5 |
| Datenbank: | OpenAIRE |
Full Text Finder 
Nájsť tento článok vo Web of Science | Abstract: | For the autonomous surface vehicle (ASV) planning problem, an enhanced A* method incorporating encrypted memory database for ASV efficient local path planning is proposed. Considering the current various path planning problems mostly use methods with high time complexity, such as neural networks, we select the A* algorithm with low time complexity as the basis. To speed up the path planning rate and further improve the real-time and realistic algorithm, this paper modifies the heuristic function of the A* algorithm by combining the motion mode of ASV. In response to the problem that the target point is far from the detection, we improve the target point design mechanism and create a new temporary target point within the detection range. In addition, the algorithm incorporates a memory database, which can record commonly used waters or retain the environmental path of navigated waters as a priori information. When the same waters are reencountered, the memory database information can be read directly to complete the navigation. Moreover, the memory database is encrypted to prevent information leakage. Finally, a simulation environment is built to verify the effectiveness of the proposed algorithm by comparison with some existing algorithms. |
|---|---|
| ISSN: | 15510018 |
| DOI: | 10.3934/mbe.2024101 |