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Componentizing autonomous underwater vehicles by physical-running algorithms

delete2024-10-25
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OA
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C
Claudio Navarro
J
José Emilio Labra Gayo
J
Jara, Francisco A. Escobar
C
Carlos Cares *
DOI:10.7717/peerj-cs.2305delete
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摘要

摘要

En 中文
Autonomous underwater vehicles (AUV) constitute a specific type of cyber-physical system that utilize electronic, mechanical, and software components. A component- based approach can address the development complexities of these systems through composable and reusable components and their integration, simplifying the development process and contributing to a more systematic, disciplined, and measurable engineering approach. In this article, we propose an architecture to design and describe the optimal performance of components for an AUV engineering process. The architecture involves a computing approach that carries out the automatic control of a testbed using genetic algorithms, where components undergo a 'physical-running' evaluation. The procedure, defined from a method engineering perspective, complements the proposed architecture by demonstrating its application. We conducted an experiment to determine the optimal operating modes of an AUV thruster with a flexible propeller using the proposed method. The results indicate that it is feasible to design and assess physical components directly using genetic algorithms in real-world settings, dispensing with the corresponding computational model and associated engineering stages for obtaining an optimized and tested operational scope. Furthermore, we have developed a cost-based model to illustrate that designing an AUV from a physical- running perspective encompasses extensive feasibility zones, where it proves to be more cost-effective than an approach based on simulation.
Keyword:
Physical-running algorithms
Cyber-physical systems
Autonomous vehicles
Genetic algorithms
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期刊

PeerJ Computer Science 封面图
PeerJ Computer Science
IF:
2.5
论文数:
3.4K
被引数:
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机构

U
University of Oviedo
学者数:
1.1W
论文数: 1.0W
被引数: 15
U
Universidad de La Frontera
学者数:
3.7K
论文数: 2.8K
被引数: 2.5K
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