Research on Path Planning Algorithm Based on D Search

Path planning algorithms are current research hotspots. Heuristic algorithms that can solve dynamic environment problems are gradually becoming the mainstream research direction. The D* algorithm, as one of the new heuristic algorithms, has the advantages of being more computationally efficient than...

Full description

Saved in:
Bibliographic Details
Published in2023 International Conference on Ocean Studies (ICOS) pp. 114 - 119
Main Authors Leng, Jiajun, Fu, Li, Wang, Lingling, Tang, Ning
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.10.2023
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:Path planning algorithms are current research hotspots. Heuristic algorithms that can solve dynamic environment problems are gradually becoming the mainstream research direction. The D* algorithm, as one of the new heuristic algorithms, has the advantages of being more computationally efficient than the A * algorithm and being able to solve dynamic problems, and has played an important role in two-dimensional path planning projects such as the NASA Mars rover. But its theoretical research and practical application in 3D environment are very rare. In this context, this paper carries out an in-depth study of the path planning problem based on the D* algorithm, and completes the theoretical derivation and simulation process in 2D and 3D environments. In this paper, a complex random 2D map is established by random generation, and the simulation of D* algorithm is realized on the map with the help of Python language, which realizes the finding of the relative optimal path. Then, in order to study the feasibility of D* solving in 3D environment, the simulation of D* algorithm under 3D map is realized with the help of Python and Unity3d. The research results show that the simulation results are good, which is valuable for the theoretical research and practical application of heuristic algorithms, and plays a role in the application of robot path planning algorithms.
DOI:10.1109/ICOS60708.2023.10425152