Year: 2025
Type: article
Abstract: The Rapidly Exploring Random Tree (RRT) is widely employed in the field of intelligent vehicles, but traditional RRT has issues like inefficient blind expansion, tortuous/discontinuous paths, and slow convergence. Thus, a multi-dimensional optimized RRT is proposed. First, a heuristic search method is adopted to reduce blind sampling, guiding sampling toward the target and cutting irrelevant searches. Second, to fix RRT’s inability to adjust step size dynamically (limiting complex road adaptability), step size is optimized based on environmental information. Third, since treating vehicles as mass points leads to unreasonable paths, sampling points are expanded for practicality. Finally, redundant points are removed via a greedy strategy, and paths are smoothed with quasi-uniform cubic B-splines to meet ride comfort needs. MATLAB R2022b simulations validate the algorithm: in simple scenarios, optimized RRT reduces sampling points to 232 (24.4% of traditional RRT), runtime to 3.25 s (79.4% cut), path length to 673.84 m (15.6% reduction); in complex scenarios, 636 points (37.0%), 11.07 s runtime (58.8% cut), 699.61 m path (21.6% reduction), outperforming traditional RRT and Q-RRT*.
Source: World Electric Vehicle Journal
DOI: 10.3390/wevj16110605
Authors: Jinbo Wang, Tongjia Pang, Weihai Zhang, Wei Liao, Tingwei Du
Institution: Shandong Jiaotong University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,689
OpenAlex ID: W4415820359