In order to improve the motion safety and interactivity of mobile robots in an indoor human-robot integration environment, a dynamic obstacle avoidance method for indoor mobile robots integrating pedestrian motion information is proposed, considering both task constraints and social rules. First, the YOLO v3 algorithm and the Deep Sort algorithm are used to detect and track pedestrians in indoor environments in real time, respectively, and calculate the historical trajectories of pedestrians in the past. Then, the Social-GAN algorithm is used to build a pedestrian interaction model to achieve trajectory prediction. On this basis, the motion state of pedestrians is integrated into the robot obstacle avoidance algorithm, the evaluation function is designed according to social rules, and the sampling speed samples of the robot are evaluated, so that the robot can bypass pedestrians in a safe and comfortable way and ensure social acceptability of mobile robots in a human-robot integration environment. Through experimental comparison and analysis, compared with the traditional DWA method, the method in this paper not only improves the efficiency of robot navigation and obstacle avoidance, but also increases the navigation obstacle avoidance time from 23. 56 s to 19. 38 s in the same indoor scene, and can effectively reduce the collision with pedestrians risk, and ensure the safety and sociality of robot navigation.