Trajectory planning for mobile robots in cluttered environments remains a major challenge due to narrow passages, where conventional methods often fail or generate suboptimal paths. To address this issue, we propose the adaptive trajectory refinement algorithm, which consists of two main stages. First, to ensure safety at the path-segment level, a segment-wise conservative collision test is applied, where risk-prone trajectory path segments are recursively subdivided until collision risks are eliminated. Second, to guarantee pose-level safety, pose correction based on penetration direction and line search is applied, ensuring that each pose in the trajectory is collision-free and maximally clear from obstacles. Simulation results demonstrate that the proposed method achieves up to 1.69x higher success rates and up to 3.79x faster planning times than state-of-the-art approaches. Furthermore, real-world experiments confirm that the robot can safely pass through narrow passages while maintaining rapid planning performance.

翻译：在杂乱环境中为移动机器人进行轨迹规划仍然是一个主要挑战，尤其是在狭窄通道中，传统方法往往失败或生成次优路径。为解决这一问题，我们提出了自适应轨迹优化算法，该算法包含两个主要阶段。首先，为确保路径段级别的安全性，采用分段保守碰撞检测，对高风险轨迹路径段进行递归细分，直至消除碰撞风险。其次，为保证位姿级别的安全性，应用基于穿透方向和线性搜索的位姿校正，确保轨迹中的每个位姿均无碰撞且与障碍物保持最大距离。仿真结果表明，与现有先进方法相比，所提方法的成功率最高可提升1.69倍，规划时间最快可缩短3.79倍。此外，真实世界实验证实，机器人能够在保持快速规划性能的同时安全通过狭窄通道。