Biased Target-tree * Algorithm with RRT * for Reducing Parking Path Planning Time
Joonwoo Ahn, Minsoo Kim, Jaeheung Park · 2023
The target-tree*algorithm, which is a variant of the optimal rapidly-exploring random tree (RRT*) has been proposed to reduce the parking path planning time. This algorithm pre-generates a set of backward paths (target-tree) around a parking spot and extends an RRT*from the initial pose until it is connected to a random sample of the target-tree. However, it is difficult to obtain the shortest (optimal) parking path within a short planning time because connected samples between the tree and the target-tree are randomly searched. To deal with this problem, this paper proposes a biased target-tree*algorithm with RRT*that searches connected random samples in a biased range near the target-tree. This range has a Gaussian distribution centered on the optimal connected sample where the shortest parking path can be obtained quickly and is obtained through supervised learning. In actual parking situations, the biased target-tree*algorithm obtained a shorter path with less length deviation than the original target-tree*algorithm within a shorter planning time.