  Mobile robots have gained increased importance within industrial tasks such
as commissioning, delivery or operation in hazardous environments. The ability
to autonomously navigate safely especially within dynamic environments, is
paramount in industrial mobile robotics. Current navigation methods depend on
preexisting static maps and are error-prone in dynamic environments.
Furthermore, for safety reasons, they often rely on hand-crafted safety
guidelines, which makes the system less flexible and slow. Visual based
navigation and high level semantics bear the potential to enhance the safety of
path planing by creating links the agent can reason about for a more flexible
navigation. On this account, we propose a reinforcement learning based local
navigation system which learns navigation behavior based solely on visual
observations to cope with highly dynamic environments. Therefore, we develop a
simple yet efficient simulator - ARENA2D - which is able to generate highly
randomized training environments and provide semantic information to train our
agent. We demonstrate enhanced results in terms of safety and robustness over a
traditional baseline approach based on the dynamic window approach.
