Socially Aware Planning for Indoor Navigation

Abstract

As robots increasingly present in human-populated spaces, they must be able to navigate among humans safely and without disrupting. People try to preserve their own personal space when moving in real-world social spaces. However, the current navigation methods do not consider this aspect and treat humans as any other obstacle.This thesis proposes a method that considers personal space for humans as well as social navigation norms. For this purpose, the robot converts camera-based human detections to a costmap form and define the personal space as a Gaussian asymmetric function. The proposed solution is validated through real-world experiments, demonstrating that the robot can improve the quality of navigation. The proposed solution is available on GitHub as a costmap layer that can be easily integrated into existing frameworks.