In response to the issues of low solution efficiency, poor path planning quality, and limited search completeness in narrow passage environments associated with Rapidly-exploring Random Tree (RRT), this paper proposes a Grid-based Variable Probability Rapidly-exploring Random Tree algorithm (GVP-RRT) for narrow passages. The algorithm introduced in this paper preprocesses the map through gridization to extract features of different path regions. Subsequently, it employs random growth with variable probability density based on the features of path regions using various strategies based on grid, probability, and guidance to enhance the probability of growth in narrow passages, thereby improving the completeness of the algorithm. Finally, the planned route is subjected to path re-optimization based on the triangle inequality principle. The simulation results demonstrate that the planning success rate of GVP-RRT in complex narrow channels is increased by 11.5–69.5% compared with other comparative algorithms, the average planning time is reduced by more than 50%, and the GVP-RRT has a shorter average planning path length.

针对快速探索随机树(RRT)在狭窄通道环境下求解效率低、路径规划质量差、搜索完整性有限的问题，提出一种基于网格的变概率快速探索随机树算法( GVP-RRT) 适用于狭窄通道。本文介绍的算法通过网格化对地图进行预处理，提取不同路径区域的特征。随后，根据路径区域的特征，采用基于网格、概率、引导等多种策略，采用变概率密度的随机增长，增强狭窄通道中的增长概率，从而提高算法的完备性。最后，基于三角不等式原理对规划路径进行路径重新优化。仿真结果表明，与其他对比算法相比，GVP-RRT在复杂狭窄通道中的规划成功率提高了11.5%~69.5%，平均规划时间缩短了50%以上，且GVP-RRT的规划时间更短。平均规划路径长度。