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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Published in:2017 International Conference on Unmanned Aircraft Systems (ICUAS)
Main Authors: Leira, Frederik Stendahl, Johansen, Tor Arne, Fossen, Thor I.
Format: Book Part
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2017
Subjects:
Online Access:http://hdl.handle.net/11250/2470616
https://doi.org/10.1109/ICUAS.2017.7991435
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spelling 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
institution Open Polar
collection NTNU Open Archive (Norwegian University of Science and Technology)
op_collection_id 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
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