Journal of System Simulation
Abstract
Abstract: To address the low planning efficiency, poor safety, and limited practicability of the RRT algorithm in global path planning within complex three-dimensional environments, which fail to meet the requirements of planning the safe flight path of UAVs, an improved SAC-RRT algorithm was proposed, which fused SAC deep reinforcement learning algorithm and RRT algorithm. A target point bias strategy and a dynamic step size based on the SAC decision-making network were designed to reduce the blindness of RRT. A random point correction process was designed to optimize the position of random points based on actions from the decision network and improve the path safety. In addition, simplified and smooth steps were designed to further improve path safety. Several 3D scenarios of varying complexity were designed, and the planning results show that the SAC-RRT algorithm reduces path length and planning time while improving path smoothness and safety.
Recommended Citation
Liang, Xiuman; Liu, Ziliang; and Liu, Zhendong
(2025)
"Path Planning of Improved RRT Algorithm Based on Deep Reinforcement Learning,"
Journal of System Simulation: Vol. 37:
Iss.
10, Article 12.
DOI: 10.16182/j.issn1004731x.joss.24-0494
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss10/12
First Page
2578
Last Page
2593
CLC
TP18; TP391.9
Recommended Citation
Liang Xiuman, Liu Ziliang, Liu Zhendong. Path Planning of Improved RRT Algorithm Based on Deep Reinforcement Learning[J]. Journal of System Simulation, 2025, 37(10): 2578-2593.
DOI
10.16182/j.issn1004731x.joss.24-0494
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