  Industrial robots are widely used in various manufacturing environments due
to their efficiency in doing repetitive tasks such as assembly or welding. A
common problem for these applications is to reach a destination without
colliding with obstacles or other robot arms. Commonly used sampling-based path
planning approaches such as RRT require long computation times, especially in
complex environments. Furthermore, the environment in which they are employed
needs to be known beforehand. When utilizing the approaches in new
environments, a tedious engineering effort in setting hyperparameters needs to
be conducted, which is time- and cost-intensive. On the other hand, Deep
Reinforcement Learning has shown remarkable results in dealing with unknown
environments, generalizing new problem instances, and solving motion planning
problems efficiently. On that account, this paper proposes a
Deep-Reinforcement-Learning-based motion planner for robotic manipulators. We
evaluated our model against state-of-the-art sampling-based planners in several
experiments. The results show the superiority of our planner in terms of path
length and execution time.
