Motion-Prediction-Based Obstacle Avoidance Method for Mobile Robots via Deep Reinforcement Learning

Abstract

Obstacle avoidance is essential for mobile robot when applying to dynamic scenarios. Existing methods that based on deep reinforcement learning (DRL) use the position information as the environment states and neural network input to train the robot in a low efficiency manner, because the position information is unable to indicate obstacle’s motion trend. To address this problem, this paper proposes a new motion-prediction-based obstacle avoidance method for mobile robots based on DRL. The position information of dynamic obstacles in time domain is used to construct a motion trend vector, and together with other motion state factors to form the robot motion guidance matrix, which effectively expresses the motion change trend of dynamic obstacles in a period that provides more valuable information for the robot to choose avoidance action. The experimental results show that the safety of avoiding dynamic obstacles is effectively improved.