A Path Planning Method Based on Simplified Visibility Graph and Support Vector Machine

The paper proposes a path planning method based on a simplified visibility graph (SVG) and support vector machine (SVM). Firstly, the centroid point set of polygon obstacles is used to construct a Delaunay triangulation network. Then, all neighbors of each obstacle are obtained from the Delaunay triangulation network to construct neighbor pairs. A global environmental SVG is then constructed by simplifying the visibility relationships between vertices of neighbor obstacle pairs. Next, the start and goal points are maintained in the SVG and the path is planned using Dijkstra’s algorithm and pruning method. Additionally, an elliptical window is generated based on the globally planned path. The obstacles and the boundary of the ellipse are classified into positive and negative samples, respectively. A safe and smooth path is obtained by training the SVM based on the radial basis function (RBF). The simulation experiments show that, compared with traditional methods, the proposed method based on the Delaunay triangulation reduces the number of visual edges by more than 40% and reduces cost time by over 50%. What’s more, the optimized path obtained through the SVM is sufficiently smooth and safe.