Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
On Restricting Real-Valued Genotypes in Evolutionary Algorithms Kapitel lesen Erstes Kapitel lesen

2021 | OriginalPaper | Buchkapitel

EA-Based ASV Trajectory Planner for Pollution Detection in Lentic Waters

verfasst von : Gonzalo Carazo-Barbero, Eva Besada-Portas, José M. Girón-Sierra, José A. López-Orozco

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This paper presents a new planner based on Evolutionary Algorithms (EAs) to optimize the trajectory of an Autonomous Surface Vehicle (ASV), equipped with a probe, that has to determine the location of a pollutant in lentic water bodies (e.g. reservoirs, dams). To achieve it, our planner 1) exploits the information provided by a simulator that determines the pollutant distribution based on the water currents and 2) is supported by an EA that optimizes the mission duration, the ASV trajectory length and the measurements taken by its probe in highly polluted areas. The current version of the planner also ensures that the trajectories are feasible from the ASV and water body perspective, and solves this constrained multi-objective problem as a mono-objective one that linearly combines the constraint and objective functions. The preliminary results over different scenarios show that the planner can already determine overall good solutions, but that needs to be modified (e.g. using a multi-objective intended EA) to improve them further.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Evolutionary Algorithms for Roughness Coefficient Estimation in River Flow Analyses
Fußnoten
1
Each of them has advantages and disadvantages. For instance, the Finite Difference Method (FDM) is simple to understand and does not require to transform the model into a variational formulation, but its implementation in irregular meshes such as the one used in our case is exceedingly difficult. Alternatively, the Finite Elements Method (FEM) and Finite Volumes Method (FVM), which are often used in problems involving fluid mechanics, are harder to understand but can use irregular meshes easily. However, all of them often present convergence problems in the calculation process due to the non-linearity of the Navier-Stokes equation.
 
2
As water is incompressible and the domain volume does not change, the outflow is forced to have the same magnitude as the inflow.
 
3
The first of these accelerations is used in a Lagrangian frame of reference to obtain the velocity of a particle over time, while the second is calculated from the fluid flow in its Eulerian description at the position of the particle. Besides, Eq. (3) is also valid for a non-stationary flow, substituting \(\varvec{u}_{ss}(\varvec{w})\) by \(\varvec{u}(\varvec{w},t)\).
 
4
Particles outside the water body are re-sampled to make them start at valid locations.
 
5
As the only requirement for using this solver is to have a different sign inside and outside of the domain function, any mathematical function that meets that criterion (including those with non-continuous images such as boolean functions) is valid. In other words, our way of proceeding simplifies immensely the definition of domains with irregular geometry.
 
6
We tested that it is quicker and more accurate to do it with Matlab than with our own trajectory discretization.
 
7
In this case, we are forced to use the Matlab function, as \(\mathrm {OF}_2\) determines the value of L, which is required to perform the proportional trajectory discretization.
 
8
In this case we do not need to slide the time vectors since the \(t_0\) of both parents always equals the initial mission time \(t_0^{mission}\).
 
9
To obtain this average, for those runs that have finished earlier than in 500 iterations we extend the last obtained value to the remaining iterations up to 500.
 
10
The center line of each shade represents the corresponding mean value while the shade width over/under the mean is the standard deviation.
 
11
We avoid drawing the mean value over 25 runs of the EV of the best solution for each configuration and iteration, since the EV values of one configuration are not comparable with the values of others, due to the change of value in \(w_{obj,2}\).
 
12
In fact, in the graphics in top row, the green lines of the particle trajectories appear after a blue section in the ASV horizontal trajectory.
 
13
Particle vertical trajectories do not become green only for having the probe at its corresponding height, because it is also necessary to have the ASV located over them.
 
Literatur
1.
2.
3.
4.
5.
Aparicio-Medrano, E.: Physical aspects explaining cyanobacteria scum formation in natural systems. Ph.D. thesis, Eindhoven (2014)
6.
Arzamendia, M., Gregor, D., Reina, D.G., Toral, S.L., Gregor, R.: Evolutionary path planning of an autonomous surface vehicle for water quality monitoring. In: International Conference on Developments in e-Systems Engineering (2016)
7.
Arzamendia, M., Gregor, D., Reina, D., Toral, S.: An evolutionary approach to constrained path planning of an autonomous surface vehicle for maximizing the covered area of Ypacarai lake. Soft. Comput. 23, 1723–1734 (2019)CrossRef
8.
Bistafa, S.R.: On the development of the Navier-Stokes equation by Navier. Revista Brasileira de Ensino de Física 40(2), 1–12 (2017)
9.
Janga-Reddy, M., Nagesh-Kumar, D.: Evolutionary algorithms, swarm intelligence methods, and their applications in water resources engineering: a state of the art review. H2Open J. 3(1), 135–188 (2020)
10.
Liua, Z., Zhanga, Y., Yua, X., Yuana, C.: USVs: an overview of developments and challenges. Ann. Rev. Control 41, 71–93 (2016)
11.
Panda, M., Das, B., Subudhi, B., Pati, B.B.: A comprehensive review of path planning algorithms for autonomous underwater vehicles. Int. J. Autom. Comput. 17, 321–352 (2020)CrossRef
12.
Ravankar, A., Ravankar, A., Kobayashi, Y., Hoshino, Y., Peng, C.: Path smoothing techniques in robot navigation: state-of-the-art, current and future challenges. Sensors 18, 3170 (2018)CrossRef
13.
Shuo, J., Yonghui, Z., Wen, R., Kebin, T.: The unmanned autonomous cruise ship for water quality monitoring and sampling. In: International Conference on Computer Systems, Electronics and Control (2017)
14.
Siyang, S., Kerdcharoen, T.: Development of unmanned surface vehicle for smart water quality inspector. In: International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (2016)
15.
Storey, M., van der Gaag, B., Burns, B.: Advances in on-line drinking water quality monitoring and early warning systems. Water Res. 42(2), 741–747 (2011)CrossRef
16.
Xia, G., Han, Z., Zhao, B., Liu, C., Wang, X.: Global path planning for unmanned surface vehicle based on improved quantum ant colony algorithm. Math. Probl. Eng. 2019, 1–30 (2019)
17.
Xiong, C., Zhou, H., Lu, D., Zeng, Z., Lian, L., Yu, C.: Rapidly-exploring adaptive sampling tree*: a sample-based path-planning algorithm for unmanned marine vehicles information gathering in variable ocean environments. Sensors 20, 2515 (2020)CrossRef
18.
Xiong, C., Chen, D., Lu, D., Zeng, Z., Lian, L.: Path planning of multiple autonomous marine vehicles for adaptive sampling using voronoi-based ant colony optimization. Robot. Auton. Syst. 115, 90–103 (2019)CrossRef
Metadaten
Titel
EA-Based ASV Trajectory Planner for Pollution Detection in Lentic Waters
verfasst von
Gonzalo Carazo-Barbero
Eva Besada-Portas
José M. Girón-Sierra
José A. López-Orozco
Copyright-Jahr
2021
Buch
Applications of Evolutionary Computation

Print ISBN: 978-3-030-72698-0

Electronic ISBN: 978-3-030-72699-7

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-72699-7_51

Premium Partner

    Bildnachweise