Deep Reinforcement Learning has emerged as an efficient dynamic obstacle avoidance method in highly dynamic environments. It has the potential to replace overly conservative or inefficient navigation approaches. However, the integration of Deep Reinforcement Learning into existing navigation systems is still an open frontier due to the myopic nature of Deep-Reinforcement-Learning-based navigation, which hinders its widespread integration into current navigation systems. In this paper, we propose the concept of an intermediate planner to interconnect novel Deep-Reinforcement-Learning-based obstacle avoidance with conventional global planning methods using waypoint generation. Therefore, we integrate different waypoint generators into existing navigation systems and compare the joint system against traditional ones. We found an increased performance in terms of safety, efficiency and path smoothness especially in highly dynamic environments.

翻译：在高度动态环境中,深强化学习已成为一种高效的动态障碍避免方法,有可能取代过于保守或低效率的导航方法;然而,将深强化学习纳入现有导航系统仍是一个开放的边疆,因为深强化学习导航的短视性质阻碍了其广泛融入现有导航系统;在本文件中,我们提出一个中间规划员的概念,将新的避免深强化学习障碍与使用中点生成的常规全球规划方法联系起来;因此,我们将不同的中点生成器纳入现有导航系统,并将联合系统与传统系统进行比较;我们发现,在安全、效率和道路畅通性方面,特别是在高度动态的环境中,绩效有所提高。