Path planning algorithm ensuring accurate localization of radiation sources-Reference-Cited by-同舟云学术

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Path planning algorithm ensuring accurate localization of radiation sources

Published:2022-01-07 Issue:8 Volume:52 Page:9574-9596
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Woller David^ORCID,Kulich Miroslav

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-021-02941-y.pdf

Reference39 articles.

1. Arain MA et al (2015) Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor. In: Sensors (switzerland), vol 15.3, pp 6845–6871. issn: 14248220, https://doi.org/10.3390/s150306845

2. Bourne JR, Pardyjak ER, Leang KK (2019) Coordinated Bayesian-Based bioinspired plume source term estimation and source seeking for mobile robots. IEEE Trans Robot 35.4:967–986. issn: 19410468. https://doi.org/10.1109/TRO.2019.2912520

3. Chen W, Liu L (2019) Pareto monte carlo tree search for multi- objective informative planning. In: Robotics: Science and systems XV. https://doi.org/10.15607/rss.2019.xv.072

4. Christie G et al (2017) Radiation search operations using scene understanding with autonomous UAV and UGV. J Field Robo 34.8:1450–1468. issn: 15564959. https://doi.org/10.1002/rob.21723

5. De Geer LE (2004) Currie detection limits in gamma-ray spectroscopy. Appl Radiat Isotopes 61.2-3:151–160. issn: 09698043. https://doi.org/10.1016/j.apradiso.2004.03.037

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1. Towards Visual Classification Under Class Ambiguity;2023 IEEE International Conference on Robotics and Automation (ICRA);2023-05-29

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