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The Kidnapped Robot Problem as a Cla...

Brennan, Elizabeth.

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The Kidnapped Robot Problem as a Classification Problem: Using Artificial Neural Networks to Characterize Robot Localization.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The Kidnapped Robot Problem as a Classification Problem: Using Artificial Neural Networks to Characterize Robot Localization./
作者:	Brennan, Elizabeth.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	275 p.
附註:	Source: Masters Abstracts International, Volume: 80-12.
Contained By:	Masters Abstracts International80-12.
標題:	Computer Engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13885072
ISBN:	9781392230886

The Kidnapped Robot Problem as a Classification Problem: Using Artificial Neural Networks to Characterize Robot Localization.
Brennan, Elizabeth.

The Kidnapped Robot Problem as a Classification Problem: Using Artificial Neural Networks to Characterize Robot Localization. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 275 p.

Source: Masters Abstracts International, Volume: 80-12.

Thesis (M.S.)--Saint Louis University, 2019.

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

Autonomous mobile robots in use cases that demand higher levels of position accuracy and update rates than Global Positioning System (GPS) can provide use localization algorithms such as Adaptive Monte Carlo Localization (AMCL) to do rapid online position estimation as they navigate a mapped environment. A localization fault that plagues these mobile robots is known as "The Kidnapped Robot Problem". If a person picks up a robot and puts the robot down somewhere else, the robot must detect that its position has been changed; otherwise, the robot's operations operate on a faulty pose estimate and the robot will malfunction or be exposed to unexpected and potentially-dangerous situations. Other "kidnapping" conditions that deteriorate the localization pose estimate by causing changes to the robot's driving model include a flat tire or wheel slip on a slick floor. Real-time detection of such kidnapping situations enables existing recovery operations to be initiated so that the robot's correct pose estimate can be reestablished. The open-source Robot Operating System (ROS) includes an implementation of AMCL that provides localization services for a variety of robot platforms and sensors. While many Kidnap Detection schemes have been designed for specific robot platforms or sensor configurations, this work proposes a novel ROS-based artificial neural network classifier for identifying Robot Kidnapping events by modeling the trends and behavior of the software internals of the Adaptive Monte Carlo Localization algorithm as implemented in the ROS amcl package. This work also developed a novel process for simulating the Kidnapped Robot Problem in the open-source Gazebo robotic simulation environment; this method can be easily customized to simulate other types of robot fault related to robot motion. The developed Kidnap Detection scheme models the probabilistic AMCL algorithm in a light-weight implementation for easy incorporation of Kidnap Detection into ROS robots to improve robot localization operations. Its usability in the ROS environment gives the detection scheme maxiumum reach and impact: as long as the ROS AMCL package is used for localization, the proposed method enables Kidnap Detection operations on a variety of robot platforms and sensors.

ISBN: 9781392230886Subjects--Topical Terms:

1567821
Computer Engineering.
Subjects--Index Terms:

AMCL

The Kidnapped Robot Problem as a Classification Problem: Using Artificial Neural Networks to Characterize Robot Localization.
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520 $a Autonomous mobile robots in use cases that demand higher levels of position accuracy and update rates than Global Positioning System (GPS) can provide use localization algorithms such as Adaptive Monte Carlo Localization (AMCL) to do rapid online position estimation as they navigate a mapped environment. A localization fault that plagues these mobile robots is known as "The Kidnapped Robot Problem". If a person picks up a robot and puts the robot down somewhere else, the robot must detect that its position has been changed; otherwise, the robot's operations operate on a faulty pose estimate and the robot will malfunction or be exposed to unexpected and potentially-dangerous situations. Other "kidnapping" conditions that deteriorate the localization pose estimate by causing changes to the robot's driving model include a flat tire or wheel slip on a slick floor. Real-time detection of such kidnapping situations enables existing recovery operations to be initiated so that the robot's correct pose estimate can be reestablished. The open-source Robot Operating System (ROS) includes an implementation of AMCL that provides localization services for a variety of robot platforms and sensors. While many Kidnap Detection schemes have been designed for specific robot platforms or sensor configurations, this work proposes a novel ROS-based artificial neural network classifier for identifying Robot Kidnapping events by modeling the trends and behavior of the software internals of the Adaptive Monte Carlo Localization algorithm as implemented in the ROS amcl package. This work also developed a novel process for simulating the Kidnapped Robot Problem in the open-source Gazebo robotic simulation environment; this method can be easily customized to simulate other types of robot fault related to robot motion. The developed Kidnap Detection scheme models the probabilistic AMCL algorithm in a light-weight implementation for easy incorporation of Kidnap Detection into ROS robots to improve robot localization operations. Its usability in the ROS environment gives the detection scheme maxiumum reach and impact: as long as the ROS AMCL package is used for localization, the proposed method enables Kidnap Detection operations on a variety of robot platforms and sensors.
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