本研究以Voronoi結構劃分為前處理步驟，結合支向機分類器構築一個起點與終點間的機器人安全平滑路徑規劃模型。Voronoi結構劃分於路徑規劃上具有快速產生路徑與路徑和障礙物間保有一定距離不發生碰撞的優點，而支向機最大化邊限原則結合其高斯核函數，兼具維持與障礙物間的安全距離，且具有產生平滑決策曲線的能力，賦予所規劃的路徑具有安全又平滑的特性。於本研究中，爲配合Voronoi結構劃分與支向機的應用，系統以一單擊函數將地圖上障礙物轉換成障礙物單點的型態，並透過額外添加的框架點與夾點，以框架點改善Voronoi路徑的準確性，加上夾點於起點與終點間限制支向機決策曲線之走向並限制其範圍。另外本研究也探討了影響系統的相關參數，詳細討論各參數對路徑安全性與平滑的影響，創新一以安全性為前提的平滑路徑規劃模型。 The paper presents a new model merging Voronoi tessellation with the optimization of support vector machine (SVM) which aims to develop a path planning for guiding a mobile robot safely and smoothly in the space with obstacles. Being a cell decomposition method for path generation, a Voronoi tessellation is employed as a preprocessor to rapidly generate a rough path in the 2-dimentional configuration space. The geometric property of Voronoi tessellation keeps the path distant away from obstacles as far as possible. Consecutively, a SVM postprocessor is used to transform the rough path into a smooth path. Based on the statistical learning theory, the large margin SVM with RBF kernel can used to generate an artificial Gaussian potential field. With the Gaussian potential field, a large margin zero-potential curve is thus obtained in the configuration space. The idea of large margin implies that a wide path can be obtained with the employment of the SVM. Sharing the merits of both planning stages, a safe and smooth path can b eventually produced. In the 2-stage path planner, obstacles can be simplified as a class of obstacle singletons for efficient computation. With the class of obstacle singletons, additional outer-frame singletons and clip points are employed to improve feasibility to draw a safe and smooth path from the starting point to the goal. Moreover, effects of corresponding parameters are detailed discussed in the paper. Plentiful planning examples are also included for approval of the feasibility of the safe and smooth path planner for real application.