近年來人工智慧與機器學習的蓬勃發展，結合了機械、控制、電子、電機與通訊技術的智慧型機器人，因此而成為了令人矚目的熱門題目。隨著生活以及工業需求，機器人已經廣泛應用於日常生活中，利用各式傳感器結合人工智慧並賦予機器人具有決策能力，使得在應用方面上獲得了更廣大的突破。而在傳統的無人機系統上，因其本地運算效能較不足，大多數皆是採用網路雲端來進行數據接收與運算，並將運算完的資訊回傳後再加以進行控制，然而網路通訊並不能保證是可信的，當數據發布給第三方以換取計算控制動作時可能會導致很大的風險，為了確保當今資訊型社會的安全，保護隱私且數據不會因駭客行為而受到損害是至關重要的。由上述概念本論文提出以秘密分享應用於模型預測控制之安全多方計算演算法，透過各式傳感器資訊建立無人機的數學運動模型，傳送數據給網路雲端來進行模型預測控制之計算，而在通訊中間使用秘密分享演算法為一種將私有數據拆分為共享的安全方法，數據所有者可以重建安全多方計算的私有結果，而其他方並不知道該結果，進而完成具有安全多方計算的模型預測控制演算法。最後經由數學證明與實驗模擬結果可以驗證其演算法具有良好的準確性以及資料的安全性。

This thesis proposes an algorithm for applying secret sharing to model predictive control system. First, the mathematical model of the omnidirectional mobile robot is established through various sensor information and the data is sent to the cloud for calculation of model predictive feedback control. The use of secret sharing in communication has been studied as a secure method of splitting private data into shares. The data owner can reconstruct the private result of the secure multi-party computation, while the other parties do not know the result. Finally, mathematical proofs and experimental simulation results can verify that the algorithm has good accuracy and data security.

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