  Recently, mobile robots have become important tools in various industries,
especially in logistics. Deep reinforcement learning emerged as an alternative
planning method to replace overly conservative approaches and promises more
efficient and flexible navigation. However, deep reinforcement learning
approaches are not suitable for long-range navigation due to their proneness to
local minima and lack of long term memory, which hinders its widespread
integration into industrial applications of mobile robotics. In this paper, we
propose a navigation system incorporating deep-reinforcement-learning-based
local planners into conventional navigation stacks for long-range navigation.
Therefore, a framework for training and testing the deep reinforcement learning
algorithms along with classic approaches is presented. We evaluated our
deep-reinforcement-learning-enhanced navigation system against various
conventional planners and found that our system outperforms them in terms of
safety, efficiency and robustness.
