GVDF-RRT*: An Improved F-RRT* Path Planning Algorithm Based on Generalized Voronoi Diagram

The sampling-based RRT* algorithm has been extensively employed for path planning. The improved variant, F-RRT*, significantly reduces the initial cost by generating new nodes; however, it also leads to increased computational time and poses challenges in narrow passage environments. To address these issues, this paper proposes an improved F-RRT* algorithm based on the Generalized Voronoi Diagram (GVD), called GVDF-RRT*. First, a sparse sampling strategy based on GVD is proposed, which improves the sampling efficiency and reduces the initial sampling nodes and initial time by guiding the random tree to explore the space quickly through GVD nodes. At the same time, the success rate of planning in narrow passage environments is improved by equalizing the sampling probability of each channel in the map. Secondly, a node direct connection mechanism is established, which reduces the redundant computation and further reduces the initial time by hierarchical processing of creation nodes, while combined with the forward optimization mechanism to optimize the paths in iteration, effectively reducing the path cost. Finally, simulations in three test environments show that, compared to RRT*, Q-RRT*, and F-RRT*, the GVDF-RRT* algorithm reduces the initial number of sampled nodes by 47.42%-76.57%, the initial time by 37.07%-86.00%, and the initial path cost by 0.47%-0.91%, while significantly improving the success rate in narrow passage environments. This further demonstrates that the proposed GVDF-RRT* significantly outperforms the baseline method in terms of initial performance and adaptability to narrow passage environments.