SocioSense: Robot Navigation Amongst Pedestrians with Social and Psychological Constraints

by   Aniket Bera, et al.

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 algorithms. We also demonstrate the benefits of our socially-aware navigation in simulated environments with tens of pedestrians.


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