Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping

Liyong Guo; Michail Antoniou; Christopher J. Baker

doi:10.1109/RadarConf2043947.2020.9266655

Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping

Liyong Guo
, Michail Antoniou
, Christopher J. Baker

Electronic, Electrical and Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The paper introduces a radar signal processing method for goal-oriented, collision-free navigation in mobile robotic platforms. The derived algorithm creates an enhanced perception of the area in front of the sensor through accumulating a sequence of radar pulses that is constantly updated, and uses previously obtained perception to inform future robot steering actions on the fly, thus creating a form of working memory. The algorithm is analytically described, and experimentally confirmed in laboratory conditions with a ground mobile robot operating in real-time.

Original language	English
Title of host publication	2020 IEEE Radar Conference, RadarConf 2020
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)	9781728189420
DOIs	https://doi.org/10.1109/RadarConf2043947.2020.9266655
Publication status	Published - 21 Sept 2020
Event	2020 IEEE Radar Conference, RadarConf 2020 - Florence, Italy Duration: 21 Sept 2020 → 25 Sept 2020

Publication series

Name	IEEE National Radar Conference - Proceedings
Volume	2020-September
ISSN (Print)	1097-5659

Conference

Conference	2020 IEEE Radar Conference, RadarConf 2020
Country/Territory	Italy
City	Florence
Period	21/09/20 → 25/09/20

Bibliographical note

Funding Information:
This work was supported in part by the National Key R&D Program of China (Grant No. 2018YFE0202101, 2018YFE0202102) and the China Scholarship Council. 978-1-7281-8942-0/20/$31.00 ©2020 IEEE

Publisher Copyright:
© 2020 IEEE.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

Cognitive radar
Collision avoidance
Map Memory
Vector Field Histogram

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/RadarConf2043947.2020.9266655

Cite this

Guo, L., Antoniou, M., & Baker, C. J. (2020). Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping. In 2020 IEEE Radar Conference, RadarConf 2020 Article 9266655 (IEEE National Radar Conference - Proceedings; Vol. 2020-September). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/RadarConf2043947.2020.9266655

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abstract = "The paper introduces a radar signal processing method for goal-oriented, collision-free navigation in mobile robotic platforms. The derived algorithm creates an enhanced perception of the area in front of the sensor through accumulating a sequence of radar pulses that is constantly updated, and uses previously obtained perception to inform future robot steering actions on the fly, thus creating a form of working memory. The algorithm is analytically described, and experimentally confirmed in laboratory conditions with a ground mobile robot operating in real-time. ",

keywords = "Cognitive radar, Collision avoidance, Map Memory, Vector Field Histogram",

author = "Liyong Guo and Michail Antoniou and Baker, \{Christopher J.\}",

note = "Funding Information: This work was supported in part by the National Key R\&D Program of China (Grant No. 2018YFE0202101, 2018YFE0202102) and the China Scholarship Council. 978-1-7281-8942-0/20/\$31.00 {\textcopyright}2020 IEEE Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 2020 IEEE Radar Conference, RadarConf 2020 ; Conference date: 21-09-2020 Through 25-09-2020",

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day = "21",

doi = "10.1109/RadarConf2043947.2020.9266655",

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Guo, L, Antoniou, M & Baker, CJ 2020, Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping. in 2020 IEEE Radar Conference, RadarConf 2020., 9266655, IEEE National Radar Conference - Proceedings, vol. 2020-September, Institute of Electrical and Electronics Engineers (IEEE), 2020 IEEE Radar Conference, RadarConf 2020, Florence, Italy, 21/09/20. https://doi.org/10.1109/RadarConf2043947.2020.9266655

Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping. / Guo, Liyong; Antoniou, Michail; Baker, Christopher J.
2020 IEEE Radar Conference, RadarConf 2020. Institute of Electrical and Electronics Engineers (IEEE), 2020. 9266655 (IEEE National Radar Conference - Proceedings; Vol. 2020-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N1 - Funding Information: This work was supported in part by the National Key R&D Program of China (Grant No. 2018YFE0202101, 2018YFE0202102) and the China Scholarship Council. 978-1-7281-8942-0/20/$31.00 ©2020 IEEE Publisher Copyright: © 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.

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AB - The paper introduces a radar signal processing method for goal-oriented, collision-free navigation in mobile robotic platforms. The derived algorithm creates an enhanced perception of the area in front of the sensor through accumulating a sequence of radar pulses that is constantly updated, and uses previously obtained perception to inform future robot steering actions on the fly, thus creating a form of working memory. The algorithm is analytically described, and experimentally confirmed in laboratory conditions with a ground mobile robot operating in real-time.

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KW - Collision avoidance

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KW - Vector Field Histogram

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PB - Institute of Electrical and Electronics Engineers (IEEE)

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ER -

Cognitive Radar System for Obstacle Avoidance Using In-Motion Memory-Aided Mapping

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this