為了讓機器人具備抓取未知物品的能力，本論文利用RGB-D影像建立物體3D模型，然後提出增強型灰狼演算法，求出多個合適的目標抓取姿態。此方法可抓取多種形狀特殊之物品，物體的三維模型是利用Kinect的深度資訊來建立。首先，將一幀幀的深度資訊會被轉換成三維點雲圖，迭代最近點演算法(ICP)進行匹配，再追蹤Kinect的即時姿態，結合多個視角的資訊，以形成空間體積的表面，最後用光線投射的方式顯示三維模型，並以此作為迭代最近點演算法之比對參考。因物體三維模型的描述相當複雜，難以實作在機器人抓取上，本論文在建立物體三維模型後，更進一步將其簡化。首先將原本的三維模型轉換成三角網格，並將這些三角網格中的頂點，以三階段的最近鄰演算法進行分類。接著，以最小平方法近似出簡化的平面。最後，再以更有效率的方式來描述這些簡化的物體平面。在建立簡化的物體三維模型後，本論文提出增強型灰狼演算法(Enhanced Grey Wolf Optimizer, EGWO)，針對一個物品，規劃多個抓取姿態，讓機器人的抓取策略更具彈性。因灰狼最佳化演算法(Grey Wolf Optimizer, GWO)的計算方法，與搜尋空間的原點有關連，若群體最佳解距離原點很遠，則表現不佳。而增強型灰狼最佳化演算法(EGWO)修正與原點有關的計算方式，並加入位置誤差項來保持演算法良好的探索與開發性能。位置誤差項是由上一刻與現在的位置差計算得出，若位置誤差大，則處於探索階段，若位置誤差小，則處於開發階段。由模擬與實驗結果所示，與GWO相比，EGWO可以得出較佳的抓取姿態，使得機器人能成功地抓取一個未知物品。

The thesis proposes an Enhanced Grey Wolf Optimizer (EGWO) to learn multiple grasping poses of unknown objects for a home service robot. For accomplishing this task, a 3D model of an object should be established at first. The depth information obtained from Kinect is converted to 3D points in every frame and is matched by Iterative Closest Point (ICP) to track the pose of Kinect. The matched points are then integrated to a volumetric surface, and the result is presented by ray casting. However, the result of 3D object model is too complex to calculate grasping pose. So that a simplified method is proposed in this thesis. The original 3D object model is transferred to a triangle mesh firstly, and the triangle vertices are classified by three-stage nearest neighbor algorithm to find surfaces of the object. Therefore, the simplified surfaces can be constructed by the least square method and the object can be described by these simplified surfaces. After established a 3D object model, this thesis proposes Enhanced Grey Wolf Optimizer (EGWO) to learn multiple grasping poses. Due to the original Grey Wolf Optimizer (GWO) is highly corresponding with the origin of searching space, the performance will be influenced by the place of global optimal. The proposed EGWO solves the problem by eliminating the influence of the origin. In addition, it adds a position error term to maintain the good exploration and exploitation. The position error term is calculated by the difference between current and previous positions. If the difference is large, the algorithm forces more on exploration. Contrarily, the algorithm forces more on exploitation. Both the simulations and robotic experiments demonstrate that EGWO provides much better performance than GWO on learning multiple grasping poses and makes the home service robot successfully grasp unknown objects.

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