In this paper, we propose a novel reinforcement learning (RL) based path
generation (RL-PG) approach for mobile robot navigation without a prior
exploration of an unknown environment. Multiple predictive path points are
dynamically generated by a deep Markov model optimized using RL approach for
robot to track. To ensure the safety when tracking the predictive points, the
robot's motion is fine-tuned by a motion fine-tuning module. Such an approach,
using the deep Markov model with RL algorithm for planning, focuses on the
relationship between adjacent path points. We analyze the benefits that our
proposed approach are more effective and are with higher success rate than
RL-Based approach DWA-RL and a traditional navigation approach APF. We deploy
our model on both simulation and physical platforms and demonstrate our model
performs robot navigation effectively and safely.

本文提出了一种基于强化学习的路径生成（RL-PG）方法，以用于移动机器人导航，无需事先探索未知环境。该方法采用深度马尔可夫模型优化的 RL 算法生成多个预测路径点，通过运动微调模块 fine-tuning 机器人的运动以确保跟踪预测点时的安全。通过在模拟和物理平台上的部署，证明本文提出的方法有效并且其成功率更高于 DWA-RL 和传统的 APF 导航方法。