An improved deep reinforcement learning for robot navigation

Abstract

Collision-free navigation is an important research direction for multi-robot systems, in which the two core problems are navigating to the target point and avoiding other robots. Many researchers use deep reinforcement learning as navigation strategy to realize multi-robot collision avoidance navigation. However, most of them use raw sensor information or global state information of the agent as the neural network input, which is not conducive to extending the navigation strategy to a larger space. This paper proposes an improved deep reinforcement learning navigation strategy, which enables robots to learn navigation and collision avoidance strategies more accurately. This strategy converts the interactive environment state from the global coordinate representation to the relative vector representation, and attenuates the influence of the rear irrelevant agents on the collision avoidance strategy. Experimental results show that the proposed method outperforms existing learning-based methods in three indicators: success rate, additional time to reach the target, and model convergence speed.