A Unified Framework for Pedestrian Trajectory Prediction and Social-Friendly Navigation

In recent years, stable robot navigation systems need to meet the requirements of comfort and sociality, such as maintaining an appropriate distance from pedestrians, avoiding crossing crowds, and so on. However, the traditional robot navigation frameworks treat the surrounding pedestrians or objects as obstacles and fail to solve the navigation problems in the context of human-robot interaction. Therefore, we propose a unified framework for human-aware and social-friendly navigation, which includes three modules: 1) pedestrian modeling, 2) trajectory prediction, and 3) path planning. In this work, we detect and model pedestrians with asymmetric Gaussian function, while introducing motion-consistent feature to identify movement group. For pedestrian trajectory prediction, we propose an efficient and accurate generative adversarial network model, combining social feature attention mechanism, and variable intention filter. For path planning, a "plan-prediction-execution" cycle mode is applied to improve the performance of mobile robots in dynamic environments. The experimental results show that compared with the traditional path planning, our social-friendly navigation framework has higher navigation efficiency and meets the comfort and sociality of social navigation.