Real-time navigation in crowded dynamic environments using Gaussian process motion control

Sungjoon Choi, Eunwoo Kim, Songhwai Oh

Research output: Contribution to journalConference articlepeer-review

36 Citations (Scopus)

Abstract

In this paper, we propose a novel Gaussian process motion controller that can navigate through a crowded dynamic environment. The proposed motion controller predicts future trajectories of pedestrians using an autoregressive Gaussian process motion model (AR-GPMM) from the partially-observable egocentric view of a robot and controls a robot using an autoregressive Gaussian process motion controller (AR-GPMC) based on predicted pedestrian trajectories. The performance of the proposed method is extensively evaluated in simulation and validated experimentally using a Pioneer 3DX mobile robot with a Microsoft Kinect sensor. In particular, the proposed method shows over 68% improvement on the collision rate compared to a reactive planner and vector field histogram (VFH).

Original languageEnglish
Article number6907322
Pages (from-to)3221-3226
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
DOIs
Publication statusPublished - 2014 Sep 22
Externally publishedYes
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: 2014 May 312014 Jun 7

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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