近年來，移動機器人的出現對於工業、研究和日常生活都加以重新塑造，推動 機器人領域進入多功能性和能力的新維度。儘管自主移動機器人前景光明，但在完 善和推進導航和控制策略以確保無縫整合到我們複雜與不斷變化的世界方面仍然存 在巨大的挑戰。因此，為了克服這些挑戰，本論文提出了一種採用斑點鬣狗優化器 (Spotted Hyena Optimizer, SHO)黑盒優化(Black-Box Optimization)技術來微調參數設 定的方法，以增強視覺同時定位與地圖建構(Visual Simultaneous Localization and Mapping, VSLAM)系統的穩健性和精確度。我們選擇使用黑盒優化技術，因為它具 有出色的多功能性，並且對系統底層功能深入了解的要求最低。這種方法具有適應 各種複雜系統的優勢，使其能夠在不同的應用中充分發揮其潛力。依靠這種多功能 方法，可以確保更廣泛的使用者可以使用該解決方案，而無需深入了解系統內部工 作的複雜性。它不僅簡化了實作，還增強了整體可用性和適用性，使其成為解決複 雜挑戰的使用者友善工具。更進一步，模型預測控制器(Model Predictive Control , MPC)也被採用來提高機器人導航的精確度和準確度。我們的方法已證明其在克服這 些複雜挑戰方面的有效性。透過模擬和實驗，系統整體性能的提升是顯而易見的。 結果凸顯了它所帶來的精確度、穩健性和適應性，使其成為解決現代系統固有複雜 性的可靠且多功能的解決方案。

In recent years, the emergence of mobile robots has reshaped industries, research, and everyday life, propelling the robotics field into new dimensions of versatility and capability. Despite the promising future of autonomous mobile robots, a formidable challenge persists in refining and advancing navigation and control strategies to ensure seamless integration into our complex and ever-changing world. Therefore, to overcome these challenges, this thesis proposes a method that employs Spotted Hyena Optimizer (SHO) black-box optimization techniques to fine-tune the parameter settings to enhance the robustness and precision of the Visual Simultaneous Localization and Mapping (VSLAM) system. We have chosen to use the black-box optimization technique due to its exceptional versatility and the minimal requirement for in-depth knowledge of the system’s underlying functions. This approach offers the advantage of adaptability to a wide range of complex systems, allowing it to harness its full potential across diverse applications. Relying on this versatile methodology ensures that the solution remains accessible to a broader spectrum of users without having to have a deep understanding of the intricacies of the system’s internal workings. Not only does it simplify implementation, but it also enhances the overall usability and applicability, making it a user-friendly tool for addressing complex challenges. Moreover, a model predictive controller (MPC) is also implemented to improve navigation of the robot with precision and accuracy. Our method has demonstrated its effectiveness in overcoming these intricate challenges. Through simulation and experimentation, an improvement in the system’s overall performance is evident. The results highlight the precision, robustness, and adaptability it brings, making it a reliable and versatile solution for addressing the complexities intrinsic in modern systems.

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