提出避障与路径优化兼顾的动态环境路径规划方法
Traversability-aware path planning in dynamic environments
- 分两步：先划分环境区域，再评估区域通行度
- 实验证明能有效避开障碍密集区并减少偏离目标
- 适合需要高安全性的移动机器人实时导航
动态环境中存在移动障碍物的路径规划仍是机器人领域的重大挑战。尽管许多研究聚焦于障碍物密集区域的导航，但通常可通过选择替代路径来避免穿越拥挤区域。本文提出一种名为Traversability-aware FMM（Tr-FMM）的路径规划方法，可在动态环境中生成路径，主动避开拥挤区域。该方法分为两步：首先对环境进行离散化处理，识别不同区域及其分布；其次计算各区域的通行度，以最小化障碍风险和目标偏离。随后通过向通行度较高的区域传播波前完成路径计算。仿真与真实场景实验表明，该方法显著提升了安全性，使机器人远离障碍物密集区，同时减少了不必要的目标偏离。
原文摘要 · Abstract (English)
Planning in environments with moving obstacles remains a significant challenge in robotics. While many works focus on navigation and path planning in obstacle-dense spaces, traversing such congested regions is often avoidable by selecting alternative routes. This paper presents Traversability-aware FMM (Tr-FMM), a path planning method that computes paths in dynamic environments, avoiding crowded regions. The method operates in two steps: first, it discretizes the environment, identifying regions and their distribution; second, it computes the traversability of regions, aiming to minimize both obstacle risks and goal deviation. The path is then computed by propagating the wavefront through regions with higher traversability. Simulated and real-world experiments demonstrate that the approach enhances significant safety by keeping the robot away from regions with obstacles while reducing unnecessary deviations from the goal.
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