CrowdNav-HERO: Pedestrian Trajectory Prediction Based Crowded Navigation with Human-Environment-Robot Ternary Fusion

Siyi Lu, Bolei Chen, Ping Zhong, Yu Sheng, Yongzheng Cui, Run Liu

Abstract: Navigating safely and efficiently in complex and crowded scenarios is a challenging problem of practical significance. A realistic and cluttered environmental layout usually significantly impacts crowd distribution and robotic motion decision-making during crowded navigation. However, previous methods almost either learn and evaluate navigation strategies in unrealistic barrier-free settings or assume that expensive features like pedestrian speed are available. Although accurately measuring pedestrian speed in large-scale scenarios is itself a difficult problem. To fully investigate the impact of static environment layouts on crowded navigation and alleviate the reliance of robots on costly features, we propose a novel crowded navigation framework with Human-Environment-Robot (HERO) ternary fusion named CrowdNav-HERO. Specifically, (i) a simulator that integrates an agent, a variable number of pedestrians, and a series of realistic environments is customized to train and evaluate crowded navigation strategies. (ii) Then, a pedestrian trajectory prediction module is introduced to eliminate the dependence of navigation strategies on pedestrian speed features. (iii) Finally, a novel crowded navigation strategy is designed by combining the pedestrian trajectory predictor and a layout feature extractor. Convincing comparative analysis and sufficient benchmark tests demonstrate the superiority of our approach in terms of success rate, collision rate, and cumulative rewards. The code is published at https://github.com/SiyiLoo/CrowdNav-HERO.