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Título : Part of the Studies in Computational Intelligence book series
Título de capítulo: Path Planning by Search Algorithms in Graph-Represented Workspaces
Autor : Venegas Perez, Ivan Dario
Autor: Montiel, Oscar
Orozco Rosas, Ulises
Palabras clave : Path planning;Knowledge representation;Graph traversal;Algorithms
Sede: Campus Tijuana
Fecha de publicación : 7-nov-2020
Citación : 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
Resumen : 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.
URI : https://repositorio.cetys.mx/handle/60000/916
ISSN : Online ISBN 978-3-030-58728-4
Print ISBN 978-3-030-58727-7
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