针对狭窄通道优化规划难题，提出自适应轨迹精修方法。
Adaptive Trajectory Refinement for Optimization-based Local Planning in Narrow Passages
- 分段保守碰撞检测，递归细分高风险路径段
- 基于穿透方向与线搜索的位姿修正，确保每点无碰撞
- 仿真与实测均显示成功率提升1.69倍、速度提升3.79倍
在复杂环境中，移动机器人面临狭窄通道的轨迹规划挑战，传统方法常失效或生成次优路径。为此，提出自适应轨迹精修算法，包含两个阶段：首先，在路径段层面采用分段保守碰撞检测，对高风险段递归细分直至消除碰撞风险；其次，在位姿层面基于穿透方向与线搜索进行位姿修正，确保轨迹中每个位姿均无碰撞且距障碍物最远。仿真结果表明，该方法成功率最高提升1.69倍，规划时间最快提升3.79倍；真实场景实验验证了机器人可在保持快速规划的同时安全通过狭窄通道。
原文摘要 · Abstract (English)
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.
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