In order to solve the problems of slow planning speed and large memory consumption of Path Planning Algorithm (RRT) for fast search random tree in the process of unmanned vehicle trajectory planning, an improved RRT algorithm combined with Artificial Potential Field Method (APF) is proposed. In this paper, a probability value is introduced into the random tree of the basic RRT algorithm to accelerate the convergence of the random tree to the target node, and then a gravity component is added to guide the random tree to grow to the target point to speed up the search process. At the same time, a repulsion field is established around the obstacle to limit the search area and reduce the randomness of the path. In this paper, we not only improve the RRT algorithm (APF-RRT), but also apply the above improvements to the RRT* algorithm (APF-RRT*). After a large number of simulation experiments, it is shown that the improved algorithm in this paper has a great improvement compared with the original algorithm and other latest improved RRT algorithms in terms of path length, optimization time and number of iterations.
LI Juntao
,
CHEN Luyao
,
HOU Xingxing
,
ZHOU Yaqi
. UAV Trajectory Planning Based on an Improved APF-RRT Algorithm[J]. Complex Systems and Complexity Science, 2026
, 23(4)
: 121
-132
.
DOI: 10.13306/j.1672-3813.2026.04.015
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