Dynamic Window Approach with Human Imitating Collision Avoidance

The autonomous navigation in the crowded environment is a challenging task due to the sensor occlusion and the complex nature of the abstract social interactions. And yet, humans are capable of navigating in such complex environment. In this paper, we propose an effective navigation method that combines the learning-based and model-based methods in a way that a cost function that includes human imitation factor learned via deep learning is integrated into the dynamic window approach (DWA) [1]. The experiments conducted on simulations show that by training the robot to imitate the human trajectory, our navigation method is safer and more efficient than the state-of-the-art methods. Additionally, we successfully deployed a physical robot in an actual environment, and we validate that our navigation quality shares similar tendency with human in the path length, travel time, and the collision avoidance.