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| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.contributor.author | Vaneges Pérez, Iván Darío | - |
| dc.contributor.author | Montiel, Oscar | - |
| dc.contributor.author | Orozco Rosas, Ulises | - |
| dc.date.accessioned | 2024-02-27T00:18:41Z | - |
| dc.date.available | 2024-02-27T00:18:41Z | - |
| dc.date.issued | 2021-11 | - |
| dc.identifier.citation | Perez, I.D.V., Montiel, O., Orozco-Rosas, U. (2021). Path Planning by Search Algorithms in Graph-Represented Workspaces. In: Melin, P., Castillo, O., Kacprzyk, J. (eds) Recent Advances of Hybrid Intelligent Systems Based on Soft Computing. Studies in Computational Intelligence, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-58728-4_4 | es_ES |
| dc.identifier.uri | https://repositorio.cetys.mx/handle/60000/1751 | - |
| dc.description | Path planning is an essential task in autonomous mobile robotics that demands to navigate following a minimum-cost path, which involves partitioning the landscape in nodes and the use of combinatorial optimization methods to find the optimal sequence of nodes to follow. Traditional algorithms such as the A* and Dijkstra are computationally efficient in landscapes with a reduced number of nodes. Most of the practical applications require to use a significantly large number of nodes up to the point that the problem might be computationally explosive. This work contributes to state-of-the-art with two heuristics for the A* algorithm that allows finding the optimal path in landscapes with a large number of nodes. The heuristics used the Euclidean and Manhattan distance in the estimation function. We present a comparative analysis of our proposal against the Dijkstra’s and A* algorithms. All the experiments were achieved using a simulation-platform specially designed for testing important algorithm features, such as the grid size, benchmark problems, the design of custom-made test sceneries, and others. Relevant results are drawn to continue working in this line. | es_ES |
| dc.language.iso | en_US | es_ES |
| dc.rights | Atribución-NoComercial-CompartirIgual 2.5 México | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/2.5/mx/ | * |
| dc.subject | Path planning | es_ES |
| dc.subject | Knowledge representation | es_ES |
| dc.subject | Graph traversal | es_ES |
| dc.subject | Algorithms | es_ES |
| dc.subject | Simulation | es_ES |
| dc.title | Recent advances of hybrid intelligent systems based on soft computin | es_ES |
| dc.type | Book chapter | es_ES |
| dc.identifier.doi | https://doi.org/10.1007/978-3-030-58728-4_4 | - |
| dc.subject.sede | Campus Tijuana | es_ES |
| dc.publisher.editorial | Springer Link | es_ES |
| dc.title.chapter | Path Planning by Search Algorithms in Graph-Represented Workspaces | es_ES |
| Aparece en las colecciones: | Capítulos de Libro | |
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