融合实时搜索与强化学习的导航规划器，提升机器人避障与运动平滑性。
Hybrid Classical/RL Local Planner for Ground Robot Navigation
- 根据环境动态切换两种规划策略：实时搜索与强化学习生成
- 在真实机器人上实现导航时间减少26%的性能提升
- 适合需要兼顾避障与运动质量的地面机器人应用
局部规划是移动机器人导航系统中的优化过程，旨在根据机器人和环境状态寻找最优速度矢量。基于优化准则与约束的不同定义，不同规划器在特定场景下表现各异。我们考虑两种概念上不同的规划器：第一种在实时中探索速度空间，具有优异的路径跟踪与运动平滑性；第二种通过强化学习训练，基于经验生成最优速度，更擅长规避动态障碍，但牺牲了运动平滑性。我们提出一种简单而有效的元推理方法，根据环境条件在两者间切换，充分发挥各自优势。在真实机器人上，我们的混合规划器在多种场景中均表现出色，相比单一规划器在导航时间上提升了26%。
原文摘要 · Abstract (English)
Local planning is an optimization process within a mobile robot navigation stack that searches for the best velocity vector, given the robot and environment state. Depending on how the optimization criteria and constraints are defined, some planners may be better than others in specific situations. We consider two conceptually different planners. The first planner explores the velocity space in real-time and has superior path-tracking and motion smoothness performance. The second planner was trained using reinforcement learning methods to produce the best velocity based on its training $"$experience$"$. It is better at avoiding dynamic obstacles but at the expense of motion smoothness. We propose a simple yet effective meta-reasoning approach that takes advantage of both approaches by switching between planners based on the surroundings. We demonstrate the superiority of our hybrid planner, both qualitatively and quantitatively, over the individual planners on a live robot in different scenarios, achieving an improvement of 26% in the navigation time.
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