  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.
