SocioSense: Robot Navigation Amongst Pedestrians with Social and Psychological Constraints
This addresses safer and more efficient robot navigation in low- to medium-density crowds, though it is incremental as it builds on existing interactive path prediction algorithms.
The paper tackles robot navigation in pedestrian crowds by incorporating social and psychological constraints, improving long-term path prediction by 21% compared to prior methods.
We present a real-time algorithm, SocioSense, for socially-aware navigation of a robot amongst pedestrians. Our approach computes time-varying behaviors of each pedestrian using Bayesian learning and Personality Trait theory. These psychological characteristics are used for long-term path prediction and generating proximic characteristics for each pedestrian. We combine these psychological constraints with social constraints to perform human-aware robot navigation in low- to medium-density crowds. The estimation of time-varying behaviors and pedestrian personalities can improve the performance of long-term path prediction by 21%, as compared to prior interactive path prediction algorithms. We also demonstrate the benefits of our socially-aware navigation in simulated environments with tens of pedestrians.