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.

翻译：工业机器人由于在装配或焊接等重复性任务中的效率,在各种制造环境中被广泛使用。这些应用的一个共同问题是,在没有与障碍或其他机器人武器碰撞的情况下到达目的地。通常使用的取样式路径规划方法,如RRT, 需要很长的计算时间。此外,需要事先知道使用它们所处的环境。在新环境中使用这些方法时,需要开展确定超参数的无聊工程工作,这是时间和成本密集型的。另一方面,深强化学习在应对未知环境、普及新问题实例和有效解决运动规划问题方面取得了显著成果。根据这一点,本文件提议为机械操纵者制定一个深重力-学习型运动规划器。我们在若干实验中对照最先进的取样规划器评估了我们的模型。结果显示我们的规划器在路径长度和执行时间方面的优势。