Path Planning Algorithm of Intelligent Vehicle Based on Improved Visibility Graphs

Presently, the visibility graphs algorithm is mainly applied for path planning of indoor mobile robot. It only considers the constraints such as travelling time and move distance. The road lane and vehicle dynamics constraints are not deal with usually. In this paper, a local path planning algorithm based on improved visibility graphs is proposed for intelligent vehicle on structured road. First, free state space (FSS) is established based on ago-vehicle state, road lane and traffic condition for permitting ago-vehicle move safely. In FSS, the vehicle’s maneuver in preview distance can be inferred and the local target point can be designated. Next, sampling points is created in FSS. Combined with local target point, initial point and sampling points, road network can be generated consequently. Then, the approachable path in the road network are evaluated by constrains of the Euclidean distance and the vehicle dynamics constraints. In this way the unique shortest path satisfying the constraints are generated as an optimal path. Finally, the efficiency and performance of the algorithm are validated by simulation results. The comparison results with RRT algorithm show that, the planned path achieved by improved visibility graphs has shorter travel distance and lower curvature, which shows more superior advantage for the path planning of intelligent vehicle than RRT.