Optimizing Robot Pathways for Energy Efficiency: Dijkstra and A* Approaches

Trajectory planning is a critical issue in the implementation of mobile robots for both human and industrial applications. Algorithms have been developed using various methodologies and heuristics to enhance their efficiency. This work presents an analysis of the Dijkstra and A* algorithms in trajectory planning for robots, focusing on energy efficiency within a simulated environment. The study considers the complexity of the search space, the cost of the route, and the number of turns in the obtained trajectory. Cost values for graphs and grids are input, and the environment is represented as a matrix. Finally, the performance of both algorithms is compared to identify the most energy-efficient one and to determine the route with the lowest cost.

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Publication Details

Published
2024-11-18
DOI
https://doi.org/10.1109/urucon63440.2024.10850049
Citations
2
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
0.70
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article

Optimizing Robot Pathways for Energy Efficiency: Dijkstra and A* Approaches

Leonel Soriano-Equigua, F. M. Maciel-Barboza, Antonio Concha, T. A. Santillán-Mata et al.
2 citations
Digital Transformation in Industry
0.70
article

Optimizing Robot Pathways for Energy Efficiency: Dijkstra and A* Approaches

Leonel Soriano-Equigua, F. M. Maciel-Barboza, Antonio Concha, T. A. Santillán-Mata, C. E. B. Goytia-Luna
article en
2 citations

Abstract

Trajectory planning is a critical issue in the implementation of mobile robots for both human and industrial applications. Algorithms have been developed using various methodologies and heuristics to enhance their efficiency. This work presents an analysis of the Dijkstra and A* algorithms in trajectory planning for robots, focusing on energy efficiency within a simulated environment. The study considers the complexity of the search space, the cost of the route, and the number of turns in the obtained trajectory. Cost values for graphs and grids are input, and the environment is represented as a matrix. Finally, the performance of both algorithms is compared to identify the most energy-efficient one and to determine the route with the lowest cost.

Universidad Autónoma de Zacatecas "Francisco García Salinas" (MX), Universidad de Colima (MX)
Affordable and clean energy
Openalex Percentile: Top 23%
Digital Transformation in Industry
0.70
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