Abstract
Autonomous underwater vehicles (AUVs) are becoming an attractive option for increasingly complex and challenging underwater search and survey operations. To meet the emerging demands of AUV mission requirements, design and tradeoff complexities, there is an increasing interest in the use of multidisciplinary design optimization (MDO) strategies. While optimization techniques have been applied successfully to a wide range of applications spanning various fields of science and engineering, there is very limited literature on optimization of AUV designs. Presented in this paper is an evolutionary approach for the design optimization of AUVs using two stochastic, population based optimization algorithms. The proposed approach is capable of modelling and solving single and multi-objective constrained formulations of the AUV design problems based on different user and mission requirements. Two formulations of the AUV design problem are considered to identify designs with minimum drag and internal clash-free assembly. The flexibility of the proposed scheme and its ability to identify optimum preliminary designs are highlighted in this paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Alam, K., Ray, T., Anavatti, S.G.: A new robust design optimization approach for unmanned underwater vehicle design. Proc. IMechE Part M: J. Engineering for the Maritime Environment 226(3), 235–249 (2012)
Alvarez, A., Bertram, V., Gualdesi, L.: Hull hydrodynamic optimization of autonomous underwater vehicles operating at snorkeling depth. Ocean Engineering 36, 105–112 (2009)
AUVAC: Autonomous Undersea Vehicle Applications Center (AUVAC) (2012), http://www.auvac.org/ (accessed May 2012)
Bandyopadhyay, S., Saha, S., Maulik, U., Deb, K.: A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Transactions on Evolutionary Computation 12(3), 269–283 (2008)
Bertram, V., Alvarez, A.: Hydrodynamic aspects of AUV design. In: The Fifth Conference on Computer and IT Applications in the Maritime Industries (COMPIT), Oegstgeest, Netherlands, pp. 45–53 (2006)
Chryssostomidis, C., Schmidt, H.: Autonomous underwater vehicles. Center for Ocean Engineering. MIT (2006), http://oe.mit.edu/content/view/126/123/ (accessed on August 7, 2010)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)
Gillmer, T., Johnson, B.: Introduction to Naval Architecture, 2nd print with revisions edn. US Naval Institute Press (1982)
Jackson, H.: MIT Professional Summer Course Submarine Design Trends (1992)
Joung, T., Sammut, K., He, F., Lee, S.K.: A study on the design optimization of an AUV by using computational fluid dynamic analysis. In: Proceedings of the Nineteenth International Offshore and Polar Engineering Conference, Osaka, Japan (2009)
Lutz, T., Wagner, S.: Numerical shape optimization of natural laminar flow bodies. In: Proceedings of 21st ICAS Congress, Melbourne, Australia (1998)
Martz, M., Neu, W.L.: Multi-objective optimization of an autonomous underwater vehicle. Marine Technology Society Journal 43(2) (2009)
Martz, M.A.: Preliminary Design of an Autonomous Underwater Vehicle using a Multiple-Objective Genetic Optimizer. Master’s thesis, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States (May 27, 2008)
Shah, V.P.: Design Considerations for Engineering Autonomous Underwater Vehicles. Master’s thesis, Massachusetts Institute of Technology (June 2007)
Singh, H.K., Isaacs, A., Ray, T., Smith, W.: Infeasibility Driven Evolutionary Algorithm (IDEA) for Engineering Design Optimization. In: Wobcke, W., Zhang, M. (eds.) AI 2008. LNCS (LNAI), vol. 5360, pp. 104–115. Springer, Heidelberg (2008)
Wang, W., Chen, X., Marburg, A., Chase, J., Hann, C.: A low-cost unmanned underwater vehicle prototype for shallow water tasks. In: Proceedings of the IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2008, Beijing, China, pp. 204–209 (2008)
Zitzler, E.: Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Shaker Verlag, Germany (1999) ISBN 3-8265-6831-1
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alam, K., Ray, T., Anavatti, S.G. (2012). An Evolutionary Approach for the Design of Autonomous Underwater Vehicles. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-35101-3_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35100-6
Online ISBN: 978-3-642-35101-3
eBook Packages: Computer ScienceComputer Science (R0)