Abstract: An improved RRT* algorithm is proposed to address the issues of low node expansion efficiency, large search space, and path curvature in the global path planning of autonomous vehicles. In this approach, an adaptive bias sampling strategy is employed to adjust the sampling points towards the goal, thereby improving the expansion quality. During the expansion phase, multiple candidate nodes are selected, with dynamic adjustment of step sizes based on actual and potential costs, enhancing the algorithm’s adaptability to different environments. After the initial path is generated, heuristic cost-based sampling is used at the nodes with the largest heuristic cost to accelerate path convergence. Finally, bidirectional path optimization based on line-of-sight checking and an improved B-spline interpolation method are applied to post-process the path, improving its smoothness. Simulation results demonstrate that the proposed algorithm significantly outperforms other algorithms of the same type in terms of path planning efficiency, path cost, and smoothness, providing a reliable solution for quickly obtaining a collision-free and smooth global optimal path for autonomous vehicles.

Key words: unmanned vehicles, global path planning, RRT* algorithm, biased sampling, heuristic expansion, variable step-size

摘要： 针对RRT*算法在无人车全局路径规划中存在节点扩展效率低、搜索范围大以及路径曲折等问题，提出了一种基于自适应偏置采样与启发式多候选扩展节点的变步长RRT*算法。该算法通过偏置公式自适应调整采样点向目标点方向，提高扩展质量；在扩展阶段选取多个候选节点，动态调整步长并结合实际与潜在代价筛选最优扩展节点，增强环境适应性；生成初步路径后，利用启发式代价最大的路径节点状态引导采样，加速路径收敛；采用视线检查的双向寻优和插值B样条方法对路径进行后处理，提升路径平滑度。仿真实验结果表明，对比同类型其他算法，改进算法在路径规划效率、路径代价以及平滑度方面具有显著优势，为无人车快速获取无碰撞且平滑的全局最优路径提供了可靠保障。

关键词: 无人车, 全局路径规划, RRT*算法, 偏置采样, 启发式扩展, 变步长