Obstacle Detection and Pathfinding for Mobile Robots
Murat Arslan · viXra · 2017
In this thesis, obstacle detection via image of objects and then pathfinding problems of NAO humanoid robot is considered. NAO's camera is used to capture the images of world map. The captured image is processed and classified into two classes; area with obstacles and area without obstacles. For classification of images, Support Vector Machine (SVM) is used. After classification the map of world is obtained as area with obstacles and area without obstacles. This map is input for path finding algorithm. In the thesis A* path finding algorithm is used to find path from the start point to the goal. The aim of this work is to implement a support vector machine based solution to robot guidance problem, visual path planning and obstacle avoidance. The used algorithms allow to detect obstacles and find an optimal path. The thesis describe basic steps of navigation of mobile robots.