@Article{cmes.2023.029152,
AUTHOR = {Chong Xu, Hao Zhu, Haotian Zhu, Jirong Wang, Qinghai Zhao},
TITLE = {Improved RRT<sup>∗</sup> Algorithm for Automatic Charging Robot Obstacle Avoidance Path Planning in Complex Environments},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {137},
YEAR = {2023},
NUMBER = {3},
PAGES = {2567--2591},
URL = {http://www.techscience.com/CMES/v137n3/53748},
ISSN = {1526-1506},
ABSTRACT = {A new and improved RRT<sup>∗</sup> algorithm has been developed to address the low efficiency of obstacle avoidance
planning and long path distances in the electric vehicle automatic charging robot arm. This algorithm enables
the robot to avoid obstacles, find the optimal path, and complete automatic charging docking. It maintains the
global completeness and path optimality of the RRT algorithm while also improving the iteration speed and quality
of generated paths in both 2D and 3D path planning. After finding the optimal path, the B-sample curve is used
to optimize the rough path to create a smoother and more optimal path. In comparison experiments, the new
algorithm yielded reductions of 35.5%, 29.2%, and 11.7% in search time and 22.8%, 19.2%, and 9% in path length for
the 3D environment. Finally, experimental validation of the automatic charging of electric vehicles was conducted
to further verify the effectiveness of the algorithm. The simulation experimental validation was carried out by
kinematic modeling and building an experimental platform. The error between the experimental results and the
simulation results is within 10%. The experimental results show the effectiveness and practicality of the algorithm.},
DOI = {10.32604/cmes.2023.029152}
}