Development of a Model for Generating Trajectories for an Autonomous Naval Vehicle Using Genetic Algorithms in MATLAB
DOI:
https://doi.org/10.15837/ijccc.2024.6.6865Keywords:
Matlab, TSP, Genetic algorithm, Cluster, Autonomous Naval VehicleAbstract
This work presents the development of a model for generating trajectories for an autonomous naval vehicle using genetic algorithms implemented in MATLAB. The primary objective is to optimize the routes the vehicle must follow, minimizing the traveled distance and ensuring efficient navigation. Various scenarios were tested by varying model parameters such as the number of environmental control points, the number of generations, and the number of individuals to evaluate the genetic algorithm’s performance. In each scenario, results were analyzed in terms of minimum traveled distance and the optimal sequence of trajectory points (FITNESS). The results show that the genetic algorithm can find efficient solutions, adapting to different configurations of points and generations. Specific examples illustrate the optimal generated trajectories, accompanied by graphical representations visualizing the sequence of points. This study demonstrates the effectiveness of genetic algorithms in route planning for autonomous naval vehicles and provides a solid foundation for future research and applications in autonomous navigation.
References
Gayo, E., Smith, N., & Vásquez, P. (2022). Environmental Injustice and Sacrifice Zones: A Case Study of Quintero Bay, Chile. Environmental Research Letters, 17(4), 045001.
Khalil, L., & Alarcon, L. (2021). Atmospheric pollutants in Quintero Bay and their impacts. Environmental Science & Pollution Research, 28(18), 22867-22881.
Muñoz, D., & Martínez, A. (2020). Industrial emissions and air quality monitoring in Quintero Bay. Journal of Environmental Management, 276, 111241.
Mertens, M., & Gawel, E. (2019). Environmental monitoring by autonomous vehicles: Challenges and opportunities. Environmental Science & Technology, 53(12), 6663-6671.
Rodrigues, J. P., & Coelho, H. (2018). Autonomous marine vehicles for environmental monitoring: Applications and technologies. Journal of Marine Systems, 180, 31-40.
Silva, M., & Costa, P. (2020). Integrating advanced technologies for autonomous marine vehicles in environmental monitoring. Ocean Engineering, 200, 107101.
Walker, B. (2018). Reducing operational costs with autonomous marine vehicles. Maritime Economics & Logistics, 20(2), 245-258.
Thompson, L., & Brown, S. (2019). Optimizing mission planning for autonomous marine vehicles. Journal of Field Robotics, 36(4), 621-634.
Green, R., & Palmer, T. (2020). Resource-efficient deployment of autonomous vehicles for environmental monitoring. IEEE Transactions on Automation Science and Engineering, 17(3), 1542- 1554.
Johnson, M., & Andrews, K. (2021). Enhancing scientific research with autonomous marine vehicles. Environmental Research Letters, 16(11), 114020.
Chen, X., & Wang, Y. (2022). Autonomous vehicles as tools for marine ecosystem research. Marine Ecology Progress Series, 667, 1-15.
Yao, J., & Zhang, W. (2020). Path planning for autonomous underwater vehicles considering ocean currents and obstacles. IEEE Transactions on Industrial Electronics, 67(6), 5181-5190.
MathWorks. (2023). Solving the Traveling Salesman Problem using Genetic Algorithm. Retrieved from https://www.mathworks.com/help/gads/genetic-algorithm.html.
Jain, A. K., & Dubes, R. C. (1988). Algorithms for Clustering Data. Prentice Hall.
Densham, P. J., & Rushton, G. (1992). A more efficient heuristic for solving large p-median problems. Geographical Analysis, 24(1), 36-46. https://doi.org/10.1068/a240289
MathWorks. (n.d.). Create distance matrix. In MATLAB documentation. Retrieved from https://www.mathworks.com/help/matlab/ref/squareform.html.
MathWorks. (n.d.). Create distance matrix. In MATLAB documentation. Retrieved from https://www.mathworks.com/help/matlab/ref/squareform.html.
Additional Files
Published
Issue
Section
License
Copyright (c) 2024 VICTOR OLIVARES, Astrid Oddershede, Luis Quezada, Manuel Vargas, Cecilia Montt
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
