Motion planning for mobile robot navigation using combine Quad-Tree Decomposition and Voronoi Diagrams

This paper presents a novel method for mobile robot navigation by visual environments where there are obstacles in the workspace. The method uses a path selection mechanism that creates innovative paths through the workspace and learns to use trajectory that are more assured. This approach is implemented on motion robots which verified the shortest path via Quad-tree Decomposition (QD) and then used Voronoi Diagrams (VD(S)) algorithm we called (Q&V) algorithm. Based on the experimental data, we claim the robot's trajectory planned by Q&V algorithm is the better find and control the roadmap is completely modeled and hasn't the localization errors. We show that even small modeled obstacles can cause large used from the preplanned path. Our complementary approach of path selection decreases the risk of path following and increases the predictability of robot's behavior.