Citation

Kim S, Guy SJ, Liu W, Wilkie D, Lau RWH, Lin MC, Manocha D. Int. J. Rob. Res. 2015; 34(2): 201-217.

Copyright

(Copyright © 2015, SAGE Publishing)

DOI

10.1177/0278364914555543

PMID

unavailable

Abstract

We introduce a novel, online method to predict pedestrian trajectories using agent-based velocity-space reasoning for improved human-robot interaction and collision-free navigation. Our formulation uses velocity obstacles to model the trajectory of each moving pedestrian in a robot's environment and improves the motion model by adaptively learning relevant parameters based on sensor data. The resulting motion model for each agent is computed using statistical inferencing techniques, including a combination of ensemble Kalman filters and a maximum-likelihood estimation algorithm. This allows a robot to learn individual motion parameters for every agent in the scene at interactive rates. We highlight the performance of our motion prediction method in real-world crowded scenarios, compare its performance with prior techniques, and demonstrate the improved accuracy of the predicted trajectories. We also adapt our approach for collision-free robot navigation among pedestrians based on noisy data and highlight the results in our simulator.

Language: en