Path planning for autonomous vehicle based on heuristic searching using online images

Abstract

In automatic navigation of mobile systems, a path network is required to enable robot/vehicle autonomous motions. Path planning is considered as a significantly important part in creating the path network and thus to be a necessary task for any autonomous vehicle system. This paper proposes a method that constructs the shortest path for vehicle auto-navigation in outdoor environments. The method using two layers of GIS information of online map images, which support to estimate not only the shape of road network but also the directed road. This is also the advantage as compared to methods, which use only aerial/satellite images. Accomplishing the estimation according to the use of this application requires several stages as follows. First, a raw road network is detected using the road map and the satellite image. Second, the road network is refined and represented by a direct graph. Third, the road network is converted into the global coordinate, which is much more convenient for performing online auto-navigation task than the other types of coordinate. Finally, the shortest path for motion is estimated by heuristic searching method based on a hybrid algorithm that is originated from Dijkstra algorithm in a combination with greedy breadth-first search algorithm. The experimental results demonstrate robustness and effectiveness of the proposed method for path network estimation under large scenes of outdoor environments.