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 |
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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 | |
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 |
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ISSN (Print) | 1050-4729 |
Other
Other | 2014 IEEE International Conference on Robotics and Automation, ICRA 2014 |
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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