Optimizing Chassis Design for Autonomous Vehicles in Challenging Environments Based on Finite Element Analysis and Genetic Algorithm
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| Pubblicato in: | ProQuest Dissertations and Theses (2025) |
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ProQuest Dissertations & Theses
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| Accesso online: | Citation/Abstract Full Text - PDF |
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| 100 | 1 | |a Sourani Yancheshmeh, Sara | |
| 245 | 1 | |a Optimizing Chassis Design for Autonomous Vehicles in Challenging Environments Based on Finite Element Analysis and Genetic Algorithm | |
| 260 | |b ProQuest Dissertations & Theses |c 2025 | ||
| 513 | |a Dissertation/Thesis | ||
| 520 | 3 | |a The demand for autonomous vehicles capable of navigating challenging terrains has necessitated the development of innovative design solutions. It emphasizes the chassis' role as a foundational framework for supporting the vehicle's body and components, requiring both the strength to endure various stresses from shock and vibration, as well as sufficient bending stiffness for improved handling. This study introduces a chassis for an innovative autonomous vehicle tailored for rough terrain navigation with a unique feature: an optimized chassis designed and equipped with quadcopter wireless charging systems. This study aimed to achieve an optimal balance between structural integrity and weight efficiency for the chassis. This is achieved through incorporating both existing theoretical knowledge and advanced analytical methods, specifically linear stress and deformation analysis. Utilizing Finite Element Methods and MATLAB, the author derived the optimum mass and length parameters for the chassis, ensuring robustness while minimizing material usage. The results indicate that this chassis not only offers a sustainable solution for rough terrain navigation but also can provide a reliable charging platform for quadcopters. This integration of advanced design techniques with practical application paves the way for a new era of autonomous vehicles tailored for specialized environments. | |
| 653 | |a Automotive engineering | ||
| 653 | |a Materials science | ||
| 653 | |a Mechanical engineering | ||
| 773 | 0 | |t ProQuest Dissertations and Theses |g (2025) | |
| 786 | 0 | |d ProQuest |t ProQuest Dissertations & Theses Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3228989688/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3228989688/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |