@article{ref1,
title="STP4: spatio temporal path planning based on pedestrian trajectory prediction in dense crowds",
journal="PeerJ Computer science",
year="2023",
author="Sato, Yuta and Sasaki, Yoko and Takemura, Hiroshi",
volume="9",
number="",
pages="e1641-e1641",
abstract="This article proposes a means of autonomous mobile robot navigation in dense crowds based on predicting pedestrians' future trajectories. The method includes a pedestrian trajectory prediction for a running mobile robot and spatiotemporal path planning for when the path crosses with pedestrians. The predicted trajectories are converted into a time series of cost maps, and the robot achieves smooth navigation without dodging to the right or left in crowds; the path planner does not require a long-term prediction. The results of an evaluation implementing this method in a real robot in a science museum show that the trajectory prediction works. Moreover, the proposed planning's arrival times is 26.4% faster than conventional 2D path planning's arrival time in a simulation of navigation in a crowd of 50 people.<p /> <p>Language: en</p>",
language="en",
issn="2376-5992",
doi="10.7717/peerj-cs.1641",
url="http://dx.doi.org/10.7717/peerj-cs.1641"
}