Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/27845

Title: Entrotaxis as a strategy for autonomous search and source reconstruction in turbulent conditions
Authors: Hutchinson, Michael
Oh, Hyondong
Chen, Wen-Hua
Keywords: Autonomous search
Sensor management
Bayesian inference
Sequential Monte Carlo
Dispersion modelling
Turbulent flow
Issue Date: 2017
Publisher: Elsevier / © The Authors
Citation: HUTCHINSON, M., OH, H. and CHEN, W-H.,2017. Entrotaxis as a strategy for autonomous search and source reconstruction in turbulent conditions. Information Fusion, 42, pp. 179-189.
Abstract: This paper proposes a strategy for performing an efficient autonomous search to find an emitting source of sporadic cues of noisy information. We focus on the search for a source of unknown strength, releasing particles into the atmosphere where turbulence can cause irregular gradients and intermittent patches of sensory cues. Bayesian inference, implemented via the sequential Monte Carlo method, is used to update posterior probability distributions of the source location and strength in response to sensor measurements. Posterior sampling is then used to approximate a reward function, leading to the manoeuvre to where the entropy of the predictive distribution is the greatest. As it is developed based on the maximum entropy sampling principle, the proposed framework is termed as Entrotaxis. We compare the performance and search behaviour of Entrotaxis with the popular Infotaxis algorithm, for searching in sparse and turbulent conditions where typical gradient-based approaches become inefficient or fail. The algorithms are assessed via Monte Carlo simulations with simulated data and an experimental dataset. Whilst outperforming the Infotaxis algorithm in most of our simulated scenarios, by achieving a faster mean search time, the proposed strategy is also more computationally efficient during the decision making process.
Description: This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/
Sponsor: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) grant number EP/K014307/1 and the MOD University Defence Research Collaboration in Signal Processing. The involvement of Dr Hyondong Oh was supported by the 2017 Research Fund (1.170013.01) of UNIST (Ulsan National Institute of Science and Technology) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2017R1D1A1B03029992).
Version: Published
DOI: 10.1016/j.inffus.2017.10.009
URI: https://dspace.lboro.ac.uk/2134/27845
Publisher Link: https://doi.org/10.1016/j.inffus.2017.10.009
ISSN: 1566-2535
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

Files associated with this item:

File Description SizeFormat
Entrotaxis.pdfPublished version1.33 MBAdobe PDFView/Open

 

SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.