An Online Interactive Approach for Crowd Navigation of Quadrupedal Robots

Robot navigation in human crowds remains the challenge of understanding human behaviors in different scenarios. We present an approach for interactive and human-friendly crowd navigation in complex static environments. The planner models the online interactions among the robot, humans, and the static environment based on game theory. It recurrently expands and optimizes the estimated trajectories for the robot and neighboring agents and provides human-friendly navigation commands. We use various indicators to evaluate the social awareness of the planners and show that our method outperforms existing approaches in success rate to reach the goals and compatibility with humans while maintaining low navigation times. The planner is successfully deployed on a real-world quadrupedal robot, demonstrating safe and interactive crowd navigation with real-time performance.