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Situational Awareness for Low Cost U...

McConnell, John.

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Situational Awareness for Low Cost Underwater Autonomy.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Situational Awareness for Low Cost Underwater Autonomy./
作者:	McConnell, John.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	120 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
標題:	Robotics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30316580
ISBN:	9798379566159

Situational Awareness for Low Cost Underwater Autonomy.
McConnell, John.

Situational Awareness for Low Cost Underwater Autonomy. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 120 p.

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

Thesis (Ph.D.)--Stevens Institute of Technology, 2023.

This item must not be sold to any third party vendors.

Underwater robots have matured over the past few decades to support the increasing need for maritime infrastructure assessment and other offshore activity. However, deploying underwater robots often involves a high startup cost associated with vehicle acquisition. It is often the case that companies not engaged in oil and gas exploration are priced out of this technology. This is primarily due to the highly accurate, often tactical-grade inertial navigation systems (INS) required to make state estimation reliable enough for long-duration autonomous operations. Moreover, sonars are the perceptual sensor of choice due to the frequent need to operate in high-turbidity water. Sonars, however, do not provide the dense 3D information required for offshore asset evaluation or autonomous navigation. In this work, we consider an underwater robot's situational awareness, i.e., the required knowledge of a robot's environment to complete its mission safely.We propose several sub-systems that assist in bridging the situational awareness gap in low-cost underwater autonomous systems equipped with imaging sonars. Firstly, we employ a stereo pair of orthogonal sonars to recover lost 3D data. Second, we generalize the method of orthogonal sensor fusion to work in large-scale environments through object-level inference. Next, we use public domain prior information to enhance the state estimation capabilities of a low-cost underwater robot. We then use submaps to recover 3D maps, providing dense 3D maps without object-level inference. Lastly, we extend our thinking to the multi-robot case. We showcase a system that performs multi-robot simultaneous localization and mapping (SLAM) using real-world underwater imaging sonar data, the first of its kind. In future work, this will enable multi-robot systems that are capable of safely operating in marine environments and cooperatively completing their tasks efficiently.{A0}

ISBN: 9798379566159Subjects--Topical Terms:

519753
Robotics.
Subjects--Index Terms:

Underwater autonomy

Situational Awareness for Low Cost Underwater Autonomy.
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500 $a Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
500 $a Advisor: Englot, Brendan.
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506 $a This item must not be sold to any third party vendors.
520 $a Underwater robots have matured over the past few decades to support the increasing need for maritime infrastructure assessment and other offshore activity. However, deploying underwater robots often involves a high startup cost associated with vehicle acquisition. It is often the case that companies not engaged in oil and gas exploration are priced out of this technology. This is primarily due to the highly accurate, often tactical-grade inertial navigation systems (INS) required to make state estimation reliable enough for long-duration autonomous operations. Moreover, sonars are the perceptual sensor of choice due to the frequent need to operate in high-turbidity water. Sonars, however, do not provide the dense 3D information required for offshore asset evaluation or autonomous navigation. In this work, we consider an underwater robot's situational awareness, i.e., the required knowledge of a robot's environment to complete its mission safely.We propose several sub-systems that assist in bridging the situational awareness gap in low-cost underwater autonomous systems equipped with imaging sonars. Firstly, we employ a stereo pair of orthogonal sonars to recover lost 3D data. Second, we generalize the method of orthogonal sensor fusion to work in large-scale environments through object-level inference. Next, we use public domain prior information to enhance the state estimation capabilities of a low-cost underwater robot. We then use submaps to recover 3D maps, providing dense 3D maps without object-level inference. Lastly, we extend our thinking to the multi-robot case. We showcase a system that performs multi-robot simultaneous localization and mapping (SLAM) using real-world underwater imaging sonar data, the first of its kind. In future work, this will enable multi-robot systems that are capable of safely operating in marine environments and cooperatively completing their tasks efficiently.{A0}
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650 4 $a Ocean engineering. $3 660731
650 4 $a Remote sensing. $3 535394
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653 $a Vehicle acquisition
653 $a Inertial navigation systems
653 $a Autonomous navigation
653 $a Orthogonal sonars
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792 $a 2023
793 $a English
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