隨著智能化生活型態的發展，同時定位與地圖構建(Simultaneous Localization and Mapping, SLAM)的研究越來越受到重視。在本研究中，將針對室內移動機器人使用四種應用於SLAM之演算法來進行模擬測試。首先，我們使用基於粒子濾波器定位的「Gmapping演算法」。在此情況下，機器人容易產生定位的偏移(Drift)，致使構建的地圖與現實環境有所落差。而且，在重取樣(Resample)階段，粒子退化(Particle Degeneracy)會造成Gmapping伴隨時間的增加而準確性退化。因此，再透過第二種方法「粒子群最佳化(Particle Swarm Optimization, PSO)」來進行測試。但是，此法有可能會陷入局部最佳解之問題。接著，本研究首先提出在重取樣階段時應用「鯨魚優化演算法(Whale Optimization Algorithm, WOA)」來做機器人的定位。其可以進行全域最佳解的搜尋，以避免落入局部最佳解，同時也能降低運算時間。除了將鯨魚優化演算法應用於SLAM之中，本研究藉著加入權重值，進一步設計了「改進型鯨魚優化演算法(Improved Whale Optimization Algorithm, IWOA)」。此權重值可用來決定進行局部的搜索或是全域的搜索，使得收斂速度加快。模擬結果顯示：以本研究提出的改進型鯨魚優化演算法進行SLAM，相比其他三種方法，所得到的地圖精確度不會較差，搜尋最佳解的收斂速度卻最快。在運算時間方面，改進型鯨魚優化演算法會因為要判斷權重值的原因，而略長於使用鯨魚優化演算法，但仍比粒子群最佳化來的快。

With the development of intelligent life styles, the research of Simultaneous Localization and Mapping (SLAM) has attracted more and more attention. In this study, four algorithms applied to SLAM will be used for simulation tests for an indoor mobile robot. First, we use “Gmapping Algorithm” based on particle filter positioning. Under this case, the robot will have a positioning offset (i.e., Drift), resulting in a gap between the constructed map and the actual environment. Moreover, in the resampling phase, Particle Degeneracy will decrease the accuracy of Gmapping as time increases. Therefore, the second method “Particle Swarm Optimization (PSO)” is used for testing. However, this method will possibly fall into the problem of local optimal solutions. Next, this research first applies “Whale Optimization Algorithm (WOA)” to the localization of the robot during the resampling phase. It is possible to search for the global optimal solution to avoid falling into the local optimal solution, and at the same time to reduce the computation time. In addition to integrating the WOA into SLAM, this research further designs “Improved Whale Optimization Algorithm (IWOA)” by adding the weight values as an improvement to WOA. The weight values can be used to determine whether to perform a local search or a global search, so that the convergence speed is accelerated. The simulation results show that while using the Improved Whale Optimization Algorithm (IWOA) proposed in this research for SLAM, compared with the other three methods, the accuracy of the mapping obtained is not worse than that by the other methods, but the convergence speed of searching for the optimal solution is the fastest. In terms of computation time, the IWOA will be slightly longer than WOA because IWOA has to determine the weight values. However, IWOA is still faster than PSO.

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