CoMet: Modeling Group Cohesion for Socially Compliant Robot Navigation in Crowded Scenes

• Published in: IEEE robotics and automation letters Vol. 7; no. 2; pp. 1008 - 1015
• Main Authors: Sathyamoorthy, Adarsh Jagan, Patel, Utsav, Paul, Moumita, Kumar, Nithish K Sanjeev, Savle, Yash, Manocha, Dinesh
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cohesion; Collision avoidance; Comets; computational geometry; Freezing; Legged locomotion; Measurement; motion and path planning; Navigation; Pedestrians; Psychology; Robots; Trajectory
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2021.3135560

We present CoMet, a novel approach for computing a group's cohesion and using that to improve a robot's navigation in crowded scenes. Our approach uses a novel cohesion-metric that builds on prior work in social psychology. We compute this metric by utilizing various visual features of pedestrians from an RGB-D camera on-board a robot. Specifically, we detect characteristics corresponding to the proximity between people, their relative walking speeds, the group size, and interactions between group members. We use our cohesion-metric to design and improve a navigation scheme that accounts for different levels of group cohesion while a robot moves through a crowd. We evaluate the precision and recall of our cohesion-metric on pedestrian datasets. We highlight the performance of our social navigation algorithm on a Turtlebot robot and demonstrate its benefits in terms of multiple metrics: freezing rate (57% decrease), deviation (35.7% decrease), and path length of the trajectory(23.2% decrease).