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Sensors 2015, 15(4), 7512-7536; doi:10.3390/s150407512

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

1
Institute of Robotics and Autonomous Systems, Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China
2
Department of Robotics, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu-Shi 525-8577, Japan
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M.N. Passaro
Received: 5 February 2015 / Revised: 19 March 2015 / Accepted: 19 March 2015 / Published: 27 March 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3679 KB, uploaded 27 March 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. View Full-Text
Keywords: chemical plume tracing; reinforcement learning; collaborative learning; behavior-based robotics chemical plume tracing; reinforcement learning; collaborative learning; behavior-based robotics
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Cao, M.-L.; Meng, Q.-H.; Wang, J.-Y.; Luo, B.; Jing, Y.-Q.; Ma, S.-G. Learning to Rapidly Re-Contact the Lost Plume in Chemical Plume Tracing. Sensors 2015, 15, 7512-7536.

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