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  3. طیف‌سنجی زیر قرمز نزدیک قابل‌ حمل و تصویربرداری ابرطیفی در تلفیق با کمومتریکس به‌ منظور تعیین ماده مؤثره دارویی در وارفارین، متفورمین و نیتروگلیسیرین
بهینه سازی توانایی یک زیرسطحی خودگردان برای یک مانور حرکتی به وسیله برنامه ریزی حرکت بهینه
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بهینه سازی توانایی یک زیرسطحی خودگردان برای یک مانور حرکتی به وسیله برنامه ریزی حرکت بهینه

صادقی سروکلایی، محمد مهدی Sadeghi Sarvkolaei, Mohammad Mahdi

Optimization the Endurance of a AUV for a Given Operation by Optimal Motion Planning

Sadeghi Sarvkolaei, Mohammad Mahdi | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58496 (08)
  4. University: Sharif University of Technology
  5. Department: Mechanical Engineering
  6. Advisor(s): Sayyaadi, Hassan
  7. Abstract:
  8. Optimizing energy consumption in autonomous underwater vehicles (AUVs) to enhance operational endurance necessitates precise ocean current prediction and intelligent path planning algorithms. In this study, real ocean current data were modeled using a hybrid neural network architecture combining two-dimensional convolutional (Conv2D) and long short-term memory (LSTM) layers to forecast the flow field. Based on the predicted currents, an integrated Dijkstra–Genetic Algorithm was employed to determine the optimal path and velocity profile at four distinct depths, considering both scenarios with and without an artificial vortex. The results demonstrate that variations in flow regimes and vortex presence significantly influence path geometry, travel time, and overall energy consumption. In shallow depths, even minor changes in current intensity lead to substantial deviations in the optimal route, whereas increasing depth tends to align the path gradually toward a straight-line trajectory. Overall, the greatest energy savings were observed in shallow-water conditions due to the stronger effect of vortex-induced currents on propulsion efficiency
  9. Keywords:
  10. Autonomous Underwater Vehicle ; Dijkstra Algorithm ; Genetic Algorithm ; Energy Consumption Optimization ; Optimal Trajectory Planning ; Hybrid Neural Network ; Long Short Term Memory (LSTM)

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