Towards Deployment of Deep-Reinforcement-Learning-Based Obstacle Avoidance into Conventional Autonomous Navigation Systems

Abstract--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. Therefore, a framework for training and testing the deep reinforcement learning algorithms along with classic approaches is presented. However, a main bottleneck is its limitation for local with multiple static and dynamic obstacles like humans, fork navigation, due to a lack a long term memory and its myopic lifts or robots. Efforts to integrate recurrent networks to mitigate dynamic environments is essential in the operation of mobile this issue result in tedious training and limited payoff.