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

ASJC Scopus subject areas

Software
Control and Systems Engineering
Artificial Intelligence
Electrical and Electronic Engineering

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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

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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).

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

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