本研究基於計算智慧與多重感測技術進行自主移動機器人的控制與導航，提出了一個整合性架構，包括：(1)路徑追蹤的控制器設計、(2)路徑規劃的演算法、(3)感測器數據融合和(4)同時定位與地圖建構(Simultaneous Localization and Mapping, SLAM)。本研究同時進行了以不同演算法優化的模擬。其方法和優點分別如下：第一、我們提出以第二類型模糊神經網絡控制器來進行路徑追蹤的控制。由阿基米德優化演算法(Archimedes Optimization Algorithm, AOA)優化的結果可以得到最低的誤差。第二、我們在路徑規劃的演算法中設計一個評估的方式。此評估方式不僅會計算路徑的最短距離，還會計算路徑與障礙物間的安全距離。由模擬結果顯示，提出的方法成功規劃平滑無稜角的路徑，並且由阿基米德優化演算法可以得到最好的評估結果。第三、在感測器數據融合的部分使用基於奇異值分解(Singular Value Decomposition, SVD)的自適應無跡的卡爾曼濾波器(Adaptive Unscented Kalman Filter, AUKF)將里程計和UWB的數據融合，並導入控制器和SLAM中輔助修正定位的計算。第四、在SLAM中使用常態分佈轉換(Normal Distributions Transform, NDT)將光學雷達(Light Detection and Ranging, LiDAR)的離散數據轉換成連續的函式。最後，以本研究所提出的整體架構來進行SLAM的模擬，可以精確地完成SLAM的任務。與其他演算法相比，在搜索最佳值的部分，阿基米德優化演算法可以得到最好的評分結果。

This study proposes an integrated architecture for the control and navigation of autonomous mobile robots based on computational intelligence and multiple sensing techniques. This integrated architecture includes (1) path tracking controller design, (2) path planning algorithms, (3) sensors’ data fusion, and (4) Simultaneous Localization and Mapping (SLAM). Simulations and implementations optimized with different algorithms were carried out in this study. The methods and advantages are as follows. First, we propose a second type of fuzzy neural network controller for path tracing control. The result optimized by the Archimedes Optimization Algorithm (AOA) will achieve the lowest error. Second, we design an evaluation method in the path planning algorithm. Such an evaluation method not only can calculate the shortest distance of the path, but also can calculate the safety distance between the path and obstacles. The simulation results show that the proposed method successfully plans a smooth path without corners, and the best evaluation result can be obtained by the AOA. Third, in the part of sensors’ data fusion, the odometer and UWB data are fused using an Adaptive Unscented Kalman Filter (AUKF) based on Singular Value Decomposition (SVD), and we import the calculation of auxiliary correction positioning in the controller and SLAM. Fourth, we use Normal Distributions Transform (NDT) in SLAM to convert the discrete data of Light Detection and Ranging (LiDAR) into a continuous function. Finally, the SLAM simulation can be carried out with the overall framework proposed in this study, and the SLAM task can be accurately completed. Compared with other algorithms, the AOA can achieve the best scoring results in the part of searching for the best value.

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