@article{SUO2022, 
author = {Chao SUO and Lile HE},
title = {Dynamic path planning strategy based on improved RRT* algorithm},
year = {2022},
journal = {Journal of Measurement Science and Instrumentation},
volume = {13},
number = {2},
pages = {198-208},
keywords = {mobile robot, path planning, rapidly-exploring random tree* (RRT*) algorithm, dynamic environment, target bias sampling},
url = {https://www.sciopen.com/article/10.62756/jmsi.1674-8042.2022023},
doi = {10.62756/jmsi.1674-8042.2022023},
abstract = {In order to solve the problem of path planning of mobile robots in a dynamic environment, an improved rapidly-exploring random tree* (RRT*) algorithm is proposed in this paper. First, the target bias sampling is introduced to reduce the randomness of the RRT* algorithm, and then the initial path planning is carried out in a static environment. Secondly, apply the path in a dynamic environment, and use the initially planned path as the path cache. When a new obstacle appears in the path, the invalid path is clipped and the path is replanned. At this time, there is a certain probability to select the point in the path cache as the new node, so that the new path maintains the trend of the original path to a greater extent. Finally, MATLABis used to carry out simulation experiments for the initial planning and replanning algorithms, respectively. More specifically, compared with the original RRT* algorithm, the simulation results show that the number of nodes used by the new improved algorithm is reduced by 43.19% on average.}
}