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Genetic algorithm for solving coverage issues in undersea mining

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Abstract
To be cost-effective, robot-based undersea mining must comply several operational constraints. Among the main constraints are the time and energy required to extract the mineral from the seabed. It is also important to reduce the wear of the joints that connect the ship on the surface with the robot crawler that does the mining on the seabed, since this not only reduces operating costs, but also lengthens the useful life of these parts which increases system security. For this reason, the least amount of twisting in these pieces is preferable, so it is advisable to reduce the number of turns or changes of direction in the trajectory of the robot that extracts the mineral. In this article, we present an algorithm to optimize Coverage Path Planning using Genetic Algorithm to produce paths with longer segments, which can be used in underwater mining and reduce the effects the mentioned turning problem. The resulting paths have on average 55% less changes of directions in the trajectory than a GA with standard cost function. In addition, in tests made by placing small obstacles in a random way, 76% of useful paths were obtained and up to 59% of useful path when the obstacles were grouped into a single larger obstacle.
Keywords
genetic algorithms, coverage path planning, deep sea mining, autonomous systems, TRAVELING SALESMAN PROBLEM, PARETO FRONTIER

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Citation

Please use this url to cite or link to this publication:

MLA
Ponguillo Intriago, Ronald Alberto, et al. “Genetic Algorithm for Solving Coverage Issues in Undersea Mining.” 2021 International Conference on Engineering and Emerging Technologies (ICEET), IEEE, 2021, pp. 838–42, doi:10.1109/ICEET53442.2021.9659708.
APA
Ponguillo Intriago, R. A., Ochoa, D., Lopez, A. J., Semanjski, I., & Gautama, S. (2021). Genetic algorithm for solving coverage issues in undersea mining. 2021 International Conference on Engineering and Emerging Technologies (ICEET), 838–842. https://doi.org/10.1109/ICEET53442.2021.9659708
Chicago author-date
Ponguillo Intriago, Ronald Alberto, Daniel Ochoa, Angel J. Lopez, Ivana Semanjski, and Sidharta Gautama. 2021. “Genetic Algorithm for Solving Coverage Issues in Undersea Mining.” In 2021 International Conference on Engineering and Emerging Technologies (ICEET), 838–42. IEEE. https://doi.org/10.1109/ICEET53442.2021.9659708.
Chicago author-date (all authors)
Ponguillo Intriago, Ronald Alberto, Daniel Ochoa, Angel J. Lopez, Ivana Semanjski, and Sidharta Gautama. 2021. “Genetic Algorithm for Solving Coverage Issues in Undersea Mining.” In 2021 International Conference on Engineering and Emerging Technologies (ICEET), 838–842. IEEE. doi:10.1109/ICEET53442.2021.9659708.
Vancouver
1.
Ponguillo Intriago RA, Ochoa D, Lopez AJ, Semanjski I, Gautama S. Genetic algorithm for solving coverage issues in undersea mining. In: 2021 International Conference on Engineering and Emerging Technologies (ICEET). IEEE; 2021. p. 838–42.
IEEE
[1]
R. A. Ponguillo Intriago, D. Ochoa, A. J. Lopez, I. Semanjski, and S. Gautama, “Genetic algorithm for solving coverage issues in undersea mining,” in 2021 International Conference on Engineering and Emerging Technologies (ICEET), Istanbul, Turkey, 2021, pp. 838–842.
@inproceedings{8725904,
  abstract     = {{To be cost-effective, robot-based undersea mining must comply several operational constraints. Among the main constraints are the time and energy required to extract the mineral from the seabed. It is also important to reduce the wear of the joints that connect the ship on the surface with the robot crawler that does the mining on the seabed, since this not only reduces operating costs, but also lengthens the useful life of these parts which increases system security. For this reason, the least amount of twisting in these pieces is preferable, so it is advisable to reduce the number of turns or changes of direction in the trajectory of the robot that extracts the mineral. In this article, we present an algorithm to optimize Coverage Path Planning using Genetic Algorithm to produce paths with longer segments, which can be used in underwater mining and reduce the effects the mentioned turning problem. The resulting paths have on average 55% less changes of directions in the trajectory than a GA with standard cost function. In addition, in tests made by placing small obstacles in a random way, 76% of useful paths were obtained and up to 59% of useful path when the obstacles were grouped into a single larger obstacle.}},
  author       = {{Ponguillo Intriago, Ronald Alberto and Ochoa, Daniel and Lopez, Angel J. and Semanjski, Ivana and Gautama, Sidharta}},
  booktitle    = {{2021 International Conference on Engineering and Emerging Technologies (ICEET)}},
  isbn         = {{9781665438971}},
  issn         = {{2409-2983}},
  keywords     = {{genetic algorithms,coverage path planning,deep sea mining,autonomous systems,TRAVELING SALESMAN PROBLEM,PARETO FRONTIER}},
  language     = {{eng}},
  location     = {{Istanbul, Turkey}},
  pages        = {{838--842}},
  publisher    = {{IEEE}},
  title        = {{Genetic algorithm for solving coverage issues in undersea mining}},
  url          = {{http://doi.org/10.1109/ICEET53442.2021.9659708}},
  year         = {{2021}},
}

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