Abstract
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 proxemic 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.
Paper
Sociosense: Robot navigation amongst pedestrians with social and psychological constraints, IROS 2017.
Aniket Bera, Tanmay Randhavane, Rohan Prinja, and Dinesh Manocha
@inproceedings{bera2017sociosense,
title={Sociosense: Robot navigation amongst pedestrians with social and psychological constraints},
author={Bera, Aniket and Randhavane, Tanmay and Prinja, Rohan and Manocha, Dinesh},
booktitle={2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={7018--7025},
year={2017},
organization={IEEE}
}