Abstract: A method of visual perception and decision making based on deep reinforcement learning was proposed, to solve the problem of malfunction and instability in the trajectory tracking and dynamic obstacle avoidance of mobile robot in a partly observable nonlinear dynamic environment. This method was used in a general form to combine the perceptual ability of convolutional neural network (CNN) with the decision-making ability of reinforcement learning. The visual perception input of environment was transformed into the direct output control of actions by the way of end-to-end learning style, so that the system environment perception and decision-making control directly formed a closed loop. The optimal decision-making strategy was acquired from the maximization of interactive cumulative reward between robot and dynamic environment. The results of simulation experiment showed that this method could meet the requirements of multi-task intelligent perception and decision making, and well solve problems of the traditional algorithm such as easily falling into local optimum, vibrating and failing to identify the path among the similar obstacles, wavering in the narrow passage and failing to reach the targets near obstacle. It greatly improved the instantaneity and adaptability of robot trajectory tracking and dynamic obstacle avoidance.

Key words: deep reinforcement learning, mobile robot, trajectory tracking, dynamic obstacle avoidance