Research on Low-Altitude UAV Path Planning by Integrating RRT and Artificial Potential Field
Junhua Yang, Yang Qi, Peng Zhang, Wei Cheng, Yang Liu, Longyuan Luan · 2025
An integrated UAV path planning framework combining Rapidly-exploring Random Tree Star (RRT*) with Artificial Potential Field (APF) methodology is proposed to address limitations inherent in conventional RRT* implementations. The hybrid algorithm employs APF-guided random tree expansion, where systematic optimization of weight coefficients and step size parameters is implemented to enhance convergence rates and search efficiency. Through comprehensive simulation analyses, the RRT*-APF method is demonstrated to achieve a$\mathbf{6 0. 3 1 \% - 6 4. 3 5 \%}$reduction in average planning time compared with baseline RRT implementations, accompanied by$\mathbf{2 3. 9 6 \% - 2 6. 3 0 \%}$decreases in path length. When benchmarked against standard RRT*, the proposed technique exhibits$\mathbf{5 1. 2 0 \% - 5 6. 7 2 \%}$improvements in computational efficiency while simultaneously demonstrating superior path smoothness characteristics. These empirical results substantiate the effectiveness of the APF integration strategy in overcoming traditional RRT* limitations related to convergence speed and trajectory quality.