近年來無人載具的發展呈現指數性的成長，不論是飛行載具或是水下載具皆有突破性的發展。由於無人載具能夠到達地形崎嶇，環境較為險惡的地區，因此自主性高的無人載具能夠降低任務於執行上的困難度。若要設計自主性高的無人載具必須有完善的自主路徑規劃演算法，有鑒於此，本文重點在於設計能夠符合不同載具運動限制需求，且具備運算效率高、總路徑距離短、與路徑平滑三大指標的路徑規劃演算法。本研究基於混合雙向搜索技術與傳統路徑規劃演算法(Goal-bias RRT*)，提出一種新的演算法，稱之為Parent Revisited Goal and Parent Bias RRT* (PRGPB-RRT*)，藉由不同的拘束條件以及不同的雙向連接方式，使前述三大性能指標皆得到顯著的改善。此外，為因應任務需求的不同，演算法必須克服環境範圍過大使運算效率變差或是任務目標臨時異動須進行路徑即時變換的問題。本研究也進一步提出藉由尋找階段性目標點的動態路徑規劃方法，使演算法只需藉由局部環境資料與目標點資訊，即可在全域環境中規劃出一條安全無碰撞的路徑。為應用方便，路徑規劃於物件避障方法也是很重要的一環。本文基於空間點雲感知的方法來偵測障礙物位置，而離散點雲不容易進行避障，據此本研究提出加入不同判斷條件的Fitting-Octree，使離散點雲能夠被精確分割成多個立方體所疊合成的障礙物地圖。研究中也加入了將點雲分群的方法，使點雲格點化及路徑規劃效率能在點數較高的環境中亦能高效實現。最後，本方法透過了真實世界中的3D點雲完成可行性驗證。實驗證明所提出方法具有效率高、路徑短且符合載具運動學限制的優點，可應用於無人載具大尺度動態路徑規劃任務，延伸應用於不同之智慧無人移動載具。

The technology of unmanned vehicles has been growing rapidly in recent years. Highly autonomous unmanned vehicles can reach rough terrain and reduce the difficulty of the mission. Based on the Goal-bias RRT*, with the aid of a bidirectional search technique, a new constrained path planning algorithm, Parent Revisited Goal and Parent Bias RRT* (PRGPB-RRT*), is proposed concerning three major objectives: shorter total distance, smoother path, and high computational efficiency. To meet different task requirements, a dynamic path planning method is proposed to diminish the high costs due to large-scale environments and keep track of the changing destination by finding local target points. In addition, object avoidance is an essential part of path planning. As a consequence, the Fitting-Octree is presented to partition the point clouds into multiple cubes representing the obstacles. Moreover, the method of grouping point clouds into clusters effectively increases the efficiency of the implementation under a large number of obstacles. Finally, the feasibility of the proposed method is verified by a real-world 3D point cloud map. The proposed method successfully enhances the efficiency as well as the trajectory, and can be applied to different intelligent unmanned vehicles.

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