Towards quantifying false alarms for effective human robot interactions

Human robot teams combining the complementary capabilities of robots and humans towards solving complex tasks are gaining wide spread popularity. Accomplishment of these tasks greatly depends on the quality of interaction between human and the robot thereby requiring models and metrics to evaluate h...

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Main Author: Mohan Rajesh Elara
Other Authors: Wijerupage Sardha Wijesoma
Format: Theses and Dissertations
Language:English
Published: 2011
Subjects:
Online Access:https://hdl.handle.net/10356/46289
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-462892023-07-04T17:07:06Z Towards quantifying false alarms for effective human robot interactions Mohan Rajesh Elara Wijerupage Sardha Wijesoma School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics Human robot teams combining the complementary capabilities of robots and humans towards solving complex tasks are gaining wide spread popularity. Accomplishment of these tasks greatly depends on the quality of interaction between human and the robot thereby requiring models and metrics to evaluate human robot interactions (HRI) in relation to performance. The traditional and most popularly adopted approach to this end has been the neglect tolerance model. The major shortcoming of this traditional model is that it presumes ideal conditions in which an operator switches control between robots sequentially based on an acceptable performance level for each robot whilst ignoring any erroneous interactions. In this thesis, the erroneous interactions that inevitably arise in HRI are identified as false alarm interactions, classified and their effects estimated. More specifically, two significant metrics that quantify the effects of false alarm interactions are defined, viz. false alarm time, and false alarm demand. In addition, the neglect tolerance model is extended to accommodate for the additional demands due to false alarm interactions. Extended neglect tolerance model is further expanded for multi-robot systems taking into account the independent or co-operating natures of robots in the team. Traditional neglect tolerance model forms the basis for fan out metric which is adopted as a general index in predicting the maximum number of robots a single operator can handle simultaneously while maintaining performance at acceptable levels. The fan out metric was redefined to account for additional demands due to the occurrence of false alarm interactions. DOCTOR OF PHILOSOPHY (EEE) 2011-11-28T08:37:32Z 2011-11-28T08:37:32Z 2011 2011 Thesis Mohan Rajesh Elara. (2011). Towards quantifying false alarms for effective human robot interactions. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/46289 10.32657/10356/46289 en 232 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics
Mohan Rajesh Elara
Towards quantifying false alarms for effective human robot interactions
description Human robot teams combining the complementary capabilities of robots and humans towards solving complex tasks are gaining wide spread popularity. Accomplishment of these tasks greatly depends on the quality of interaction between human and the robot thereby requiring models and metrics to evaluate human robot interactions (HRI) in relation to performance. The traditional and most popularly adopted approach to this end has been the neglect tolerance model. The major shortcoming of this traditional model is that it presumes ideal conditions in which an operator switches control between robots sequentially based on an acceptable performance level for each robot whilst ignoring any erroneous interactions. In this thesis, the erroneous interactions that inevitably arise in HRI are identified as false alarm interactions, classified and their effects estimated. More specifically, two significant metrics that quantify the effects of false alarm interactions are defined, viz. false alarm time, and false alarm demand. In addition, the neglect tolerance model is extended to accommodate for the additional demands due to false alarm interactions. Extended neglect tolerance model is further expanded for multi-robot systems taking into account the independent or co-operating natures of robots in the team. Traditional neglect tolerance model forms the basis for fan out metric which is adopted as a general index in predicting the maximum number of robots a single operator can handle simultaneously while maintaining performance at acceptable levels. The fan out metric was redefined to account for additional demands due to the occurrence of false alarm interactions.
author2 Wijerupage Sardha Wijesoma
author_facet Wijerupage Sardha Wijesoma
Mohan Rajesh Elara
format Theses and Dissertations
author Mohan Rajesh Elara
author_sort Mohan Rajesh Elara
title Towards quantifying false alarms for effective human robot interactions
title_short Towards quantifying false alarms for effective human robot interactions
title_full Towards quantifying false alarms for effective human robot interactions
title_fullStr Towards quantifying false alarms for effective human robot interactions
title_full_unstemmed Towards quantifying false alarms for effective human robot interactions
title_sort towards quantifying false alarms for effective human robot interactions
publishDate 2011
url https://hdl.handle.net/10356/46289
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