Citation

Hernández-Aceituno J, Acosta L, Piñeiro JD. PLoS One 2015; 10(3): e0119930.

Affiliation

Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, La Laguna, Canary Islands, Spain.

Copyright

(Copyright © 2015, Public Library of Science)

DOI

10.1371/journal.pone.0119930

PMID

25799557

Abstract

When computing the trajectory of an autonomous vehicle, inevitable collision states must be avoided at all costs, so no harm comes to the device or pedestrians around it. In dynamic environments, considering collisions as binary events is risky and inefficient, as the future position of moving obstacles is unknown. We introduce a time-dependent probabilistic collision state checker system, which traces a safe route with a minimum collision probability for a robot. We apply a sequential Bayesian model to calculate approximate predictions of the movement patterns of the obstacles, and define a time-dependent variation of the Dijkstra algorithm to compute statistically safe trajectories through a crowded area. We prove the efficiency of our methods through experimentation, using a self-guided robotic device.

Language: en