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

Sungjoon Choi; Eunwoo Kim; Songhwai Oh

doi:10.1109/ICRA.2014.6907322

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

Sungjoon Choi, Eunwoo Kim, Songhwai Oh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

39 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 language	English
Title of host publication	Proceedings - IEEE International Conference on Robotics and Automation
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3221-3226
Number of pages	6
ISBN (Electronic)	9781479936854, 9781479936854
DOIs	https://doi.org/10.1109/ICRA.2014.6907322
Publication status	Published - 2014 Sept 22
Externally published	Yes
Event	2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China Duration: 2014 May 31 → 2014 Jun 7

Publication series

Name	Proceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)	1050-4729

Other

Other	2014 IEEE International Conference on Robotics and Automation, ICRA 2014
Country/Territory	China
City	Hong Kong
Period	14/5/31 → 14/6/7

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

ASJC Scopus subject areas

Software
Control and Systems Engineering
Artificial Intelligence
Electrical and Electronic Engineering

Access to Document

10.1109/ICRA.2014.6907322

Cite this

Choi, S., Kim, E., & Oh, S. (2014). Real-time navigation in crowded dynamic environments using Gaussian process motion control. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 3221-3226). Article 6907322 (Proceedings - IEEE International Conference on Robotics and Automation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2014.6907322

Real-time navigation in crowded dynamic environments using Gaussian process motion control. / Choi, Sungjoon; Kim, Eunwoo; Oh, Songhwai.
Proceedings - IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc., 2014. p. 3221-3226 6907322 (Proceedings - IEEE International Conference on Robotics and Automation).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Choi, S, Kim, E & Oh, S 2014, Real-time navigation in crowded dynamic environments using Gaussian process motion control. in Proceedings - IEEE International Conference on Robotics and Automation., 6907322, Proceedings - IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers Inc., pp. 3221-3226, 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, Hong Kong, China, 14/5/31. https://doi.org/10.1109/ICRA.2014.6907322

Choi S, Kim E, Oh S. Real-time navigation in crowded dynamic environments using Gaussian process motion control. In Proceedings - IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc. 2014. p. 3221-3226. 6907322. (Proceedings - IEEE International Conference on Robotics and Automation). doi: 10.1109/ICRA.2014.6907322

@inproceedings{754615dd94d94fc89d361626eb42cb66,

title = "Real-time navigation in crowded dynamic environments using Gaussian process motion control",

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).",

author = "Sungjoon Choi and Eunwoo Kim and Songhwai Oh",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE International Conference on Robotics and Automation, ICRA 2014 ; Conference date: 31-05-2014 Through 07-06-2014",

year = "2014",

month = sep,

day = "22",

doi = "10.1109/ICRA.2014.6907322",

language = "English",

series = "Proceedings - IEEE International Conference on Robotics and Automation",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "3221--3226",

booktitle = "Proceedings - IEEE International Conference on Robotics and Automation",

}

TY - GEN

T1 - Real-time navigation in crowded dynamic environments using Gaussian process motion control

AU - Choi, Sungjoon

AU - Kim, Eunwoo

AU - Oh, Songhwai

PY - 2014/9/22

Y1 - 2014/9/22

N2 - 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).

AB - 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).

UR - http://www.scopus.com/inward/record.url?scp=84929180240&partnerID=8YFLogxK

U2 - 10.1109/ICRA.2014.6907322

DO - 10.1109/ICRA.2014.6907322

M3 - Conference contribution

AN - SCOPUS:84929180240

T3 - Proceedings - IEEE International Conference on Robotics and Automation

SP - 3221

EP - 3226

BT - Proceedings - IEEE International Conference on Robotics and Automation

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE International Conference on Robotics and Automation, ICRA 2014

Y2 - 31 May 2014 through 7 June 2014

ER -

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

Abstract

Publication series

Other

Bibliographical note

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this