PeerJ Computer Science (Oct 2023)

STP4: spatio temporal path planning based on pedestrian trajectory prediction in dense crowds

  • Yuta Sato,
  • Yoko Sasaki,
  • Hiroshi Takemura

DOI
https://doi.org/10.7717/peerj-cs.1641
Journal volume & issue
Vol. 9
p. e1641

Abstract

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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.

Keywords