Following Social Groups: Socially Compliant Autonomous Navigation in Dense Crowds
This addresses the problem of natural and safe robot navigation in crowded environments for robotics applications, but it is incremental as it builds on existing learning-based methods.
The paper tackles socially compliant navigation for autonomous robots in dense crowds by tracking and joining social groups, achieving safe navigation in densely populated areas with over 10 people in a 10m x 20m space.
In densely populated environments, socially compliant navigation is critical for autonomous robots as driving close to people is unavoidable. This manner of social navigation is challenging given the constraints of human comfort and social rules. Traditional methods based on hand-craft cost functions to achieve this task have difficulties to operate in the complex real world. Other learning-based approaches fail to address the naturalness aspect from the perspective of collective formation behaviors. We present an autonomous navigation system capable of operating in dense crowds and utilizing information of social groups. The underlying system incorporates a deep neural network to track social groups and join the flow of a social group in facilitating the navigation. A collision avoidance layer in the system further ensures navigation safety. In experiments, our method generates socially compliant behaviors as state-of-the-art methods. More importantly, the system is capable of navigating safely in a densely populated area (10+ people in a 10m x 20m area) following crowd flows to reach the goal.