The hierarchy of global and local planners is one of the most commonly utilized system designs in autonomous robot navigation. While the global planner generates a reference path from the current to goal locations based on the pre-built map, the local planner produces a kinodynamic trajectory to follow the reference path while avoiding perceived obstacles. To account for unforeseen or dynamic obstacles not present on the pre-built map, ``when to replan'' the reference path is critical for the success of safe and efficient navigation. However, determining the ideal timing to execute replanning in such partially unknown environments still remains an open question. In this work, we first conduct an extensive simulation experiment to compare several common replanning strategies and confirm that effective strategies are highly dependent on the environment as well as the global and local planners. Based on this insight, we then derive a new adaptive replanning strategy based on deep reinforcement learning, which can learn from experience to decide appropriate replanning timings in the given environment and planning setups. Our experimental results show that the proposed replanner can perform on par or even better than the current best-performing strategies in multiple situations regarding navigation robustness and efficiency.

翻译：全局与局部规划器的层级结构是自主机器人导航中最常用的系统设计之一。全局规划器基于预先构建的地图生成从当前位置到目标位置的参考路径，而局部规划器则生成运动动力学轨迹以跟随参考路径，同时避开感知到的障碍物。为应对预建地图中未出现的未知或动态障碍物，参考路径的“何时重新规划”对于安全高效导航的成功至关重要。然而，在部分未知环境中确定执行重规划的理想时机仍是一个未解问题。本研究首先通过大量仿真实验比较了几种常见的重规划策略，证实有效策略高度依赖于环境以及全局与局部规划器的选择。基于这一发现，我们提出了一种基于深度强化学习的新型自适应重规划策略，该策略能够从经验中学习，在给定环境和规划配置下决定合适的重规划时机。实验结果表明，在多种导航鲁棒性和效率场景中，所提重规划器的性能可与当前最优策略相媲美甚至更优。