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Learning to Rapidly Re-Contact the Lost Plume in Chemical Plume Tracing

Authors :
Meng-Li Cao
Qing-Hao Meng
Jia-Ying Wang
Bing Luo
Ya-Qi Jing
Shu-Gen Ma
Source :
Sensors, Vol 15, Iss 4, Pp 7512-7536 (2015)
Publication Year :
2015
Publisher :
MDPI AG, 2015.

Abstract

Maintaining contact between the robot and plume is significant in chemical plume tracing (CPT). In the time immediately following the loss of chemical detection during the process of CPT, Track-Out activities bias the robot heading relative to the upwind direction, expecting to rapidly re-contact the plume. To determine the bias angle used in the Track-Out activity, we propose an online instance-based reinforcement learning method, namely virtual trail following (VTF). In VTF, action-value is generalized from recently stored instances of successful Track-Out activities. We also propose a collaborative VTF (cVTF) method, in which multiple robots store their own instances, and learn from the stored instances, in the same database. The proposed VTF and cVTF methods are compared with biased upwind surge (BUS) method, in which all Track-Out activities utilize an offline optimized universal bias angle, in an indoor environment with three different airflow fields. With respect to our experimental conditions, VTF and cVTF show stronger adaptability to different airflow environments than BUS, and furthermore, cVTF yields higher success rates and time-efficiencies than VTF.

Details

Language :
English
ISSN :
14248220
Volume :
15
Issue :
4
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.68f56c141bd3489e85569ae0041a317c
Document Type :
article
Full Text :
https://doi.org/10.3390/s150407512