@article{ref1,
title="A navigation probability map in pedestrian dynamic environment based on influencer recognition model",
journal="Sensors (Basel)",
year="2021",
author="Qiao, Zhi and Zhao, Lijun and Jiang, Xinkai and Gu, Le and Li, Ruifeng",
volume="21",
number="1",
pages="e19-e19",
abstract="One of the challenging problems in robot navigation is efficient and safe planning in a highly dynamic environment, where the robot is required to understand  pedestrian patterns in the environment, such as train station. The rapid movement of  pedestrians makes the robot more difficult to solve the collision problem. In this  paper, we propose a navigation probability map to solve the pedestrians' rapid  movement problem based on the influencer recognition model (IRM). The influencer  recognition model (IRM) is a data-driven model to infer a distribution over possible  causes of pedestrian's turning. With this model, we can obtain a navigation  probability map by analyzing the changes in the effective pedestrian trajectory. Finally, we combined navigation probability map and artificial potential field (APF)  method to propose a robot navigation method and verified it on our data-set, which  is an unobstructed, overlooked pedestrians' data-set collected by us.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s21010019",
url="http://dx.doi.org/10.3390/s21010019"
}