A UAV Ice Tracking Framework for Autonomous Sea Ice Management
This paper describes an unmanned aerial vehicle (UAV) ice tracking framework for use in sea ice management applications. The framework is intended to be used in an ice management scenario where the UAV should detect and track the movement of icebergs and ice floes in an Arctic environment, and seeks...
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ftntnutrondheimi:oai:ntnuopen.ntnu.no:11250/2470616 2023-05-15T15:12:29+02:00 A UAV Ice Tracking Framework for Autonomous Sea Ice Management Leira, Frederik Stendahl Johansen, Tor Arne Fossen, Thor I. 2017 http://hdl.handle.net/11250/2470616 https://doi.org/10.1109/ICUAS.2017.7991435 eng eng Institute of Electrical and Electronics Engineers (IEEE) 2017 International Conference on Unmanned Aircraft Systems Norges forskningsråd: 223254 urn:isbn:978-1-5090-4495-5 http://hdl.handle.net/11250/2470616 https://doi.org/10.1109/ICUAS.2017.7991435 cristin:1525322 Chapter Peer reviewed 2017 ftntnutrondheimi https://doi.org/10.1109/ICUAS.2017.7991435 2019-09-17T06:53:20Z This paper describes an unmanned aerial vehicle (UAV) ice tracking framework for use in sea ice management applications. The framework is intended to be used in an ice management scenario where the UAV should detect and track the movement of icebergs and ice floes in an Arctic environment, and seeks to enable the UAV to do so autonomously. This is achieved by using an occupancy grid map algorithm and a locations of interest generator coupled with a Model Predictive Control (MPC) UAV path planner. The main contribution of this paper is interfacing the occupancy grid map algorithm with a machine vision object detection module in order to enable the UAV to generate an occupancy grid map of a pre-defined search area in real-time using on-board processing of UAV sensor data. Further, the paper presents a locations of interest generator module which generates locations that the UAV should investigate based on the generated occupancy grid map. These locations of interest are then used by an MPC path planner in order to make the UAV autonomously investigate and track ice features at said locations. Furthermore, the paper verifies the use of the developed ice tracking framework for autonomously detecting and tracking ice features based on thermal images captured with a UAV, as well as verifying the usefulness and role of UAVs in ice management scenarios by conducting two flight experiments. acceptedVersion © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Book Part Arctic Iceberg* Sea ice NTNU Open Archive (Norwegian University of Science and Technology) Arctic 2017 International Conference on Unmanned Aircraft Systems (ICUAS) 581 590 |
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NTNU Open Archive (Norwegian University of Science and Technology) |
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ftntnutrondheimi |
language |
English |
description |
This paper describes an unmanned aerial vehicle (UAV) ice tracking framework for use in sea ice management applications. The framework is intended to be used in an ice management scenario where the UAV should detect and track the movement of icebergs and ice floes in an Arctic environment, and seeks to enable the UAV to do so autonomously. This is achieved by using an occupancy grid map algorithm and a locations of interest generator coupled with a Model Predictive Control (MPC) UAV path planner. The main contribution of this paper is interfacing the occupancy grid map algorithm with a machine vision object detection module in order to enable the UAV to generate an occupancy grid map of a pre-defined search area in real-time using on-board processing of UAV sensor data. Further, the paper presents a locations of interest generator module which generates locations that the UAV should investigate based on the generated occupancy grid map. These locations of interest are then used by an MPC path planner in order to make the UAV autonomously investigate and track ice features at said locations. Furthermore, the paper verifies the use of the developed ice tracking framework for autonomously detecting and tracking ice features based on thermal images captured with a UAV, as well as verifying the usefulness and role of UAVs in ice management scenarios by conducting two flight experiments. acceptedVersion © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
format |
Book Part |
author |
Leira, Frederik Stendahl Johansen, Tor Arne Fossen, Thor I. |
spellingShingle |
Leira, Frederik Stendahl Johansen, Tor Arne Fossen, Thor I. A UAV Ice Tracking Framework for Autonomous Sea Ice Management |
author_facet |
Leira, Frederik Stendahl Johansen, Tor Arne Fossen, Thor I. |
author_sort |
Leira, Frederik Stendahl |
title |
A UAV Ice Tracking Framework for Autonomous Sea Ice Management |
title_short |
A UAV Ice Tracking Framework for Autonomous Sea Ice Management |
title_full |
A UAV Ice Tracking Framework for Autonomous Sea Ice Management |
title_fullStr |
A UAV Ice Tracking Framework for Autonomous Sea Ice Management |
title_full_unstemmed |
A UAV Ice Tracking Framework for Autonomous Sea Ice Management |
title_sort |
uav ice tracking framework for autonomous sea ice management |
publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
publishDate |
2017 |
url |
http://hdl.handle.net/11250/2470616 https://doi.org/10.1109/ICUAS.2017.7991435 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Iceberg* Sea ice |
genre_facet |
Arctic Iceberg* Sea ice |
op_relation |
2017 International Conference on Unmanned Aircraft Systems Norges forskningsråd: 223254 urn:isbn:978-1-5090-4495-5 http://hdl.handle.net/11250/2470616 https://doi.org/10.1109/ICUAS.2017.7991435 cristin:1525322 |
op_doi |
https://doi.org/10.1109/ICUAS.2017.7991435 |
container_title |
2017 International Conference on Unmanned Aircraft Systems (ICUAS) |
container_start_page |
581 |
op_container_end_page |
590 |
_version_ |
1766343153838718976 |