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A Learning-Based Semi-Autonomous Con...

Doroodgar, Barzin.

A Learning-Based Semi-Autonomous Control Architecture for Robotic Exploration of Search and Rescue Environments.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A Learning-Based Semi-Autonomous Control Architecture for Robotic Exploration of Search and Rescue Environments./
Author:	Doroodgar, Barzin.
Description:	92 p.
Notes:	Source: Masters Abstracts International, Volume: 50-05, page: .
Contained By:	Masters Abstracts International50-05.
Subject:	Engineering, Mechanical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR76521
ISBN:	9780494765210

A Learning-Based Semi-Autonomous Control Architecture for Robotic Exploration of Search and Rescue Environments.
Doroodgar, Barzin.

A Learning-Based Semi-Autonomous Control Architecture for Robotic Exploration of Search and Rescue Environments. - 92 p.

Source: Masters Abstracts International, Volume: 50-05, page: .

Thesis (M.A.Sc.)--University of Toronto (Canada), 2011.

Semi-autonomous control schemes can address the limitations of both teleoperation and fully autonomous robotic control of rescue robots in disaster environments by allowing cooperation and task sharing between a human operator and a robot with respect to tasks such as navigation, exploration and victim identification. Herein, a unique hierarchical reinforcement learning (HRL) -based semi-autonomous control architecture is presented for rescue robots operating in unknown and cluttered urban search and rescue (USAR) environments. The aim of the controller is to allow a rescue robot to continuously learn from its own experiences in an environment in order to improve its overall performance in exploration of unknown disaster scenes. A new direction-based exploration technique and a rubble pile categorization technique are integrated into the control architecture for exploration of unknown rubble filled environments. Both simulations and physical experiments in USAR-like environments verify the robustness of the proposed control architecture.

ISBN: 9780494765210Subjects--Topical Terms:

845387
Engineering, Mechanical.

A Learning-Based Semi-Autonomous Control Architecture for Robotic Exploration of Search and Rescue Environments.
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020 $a 9780494765210
035 $a (UMI)AAIMR76521
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100 1 $a Doroodgar, Barzin. $3 845705
245 1 2 $a A Learning-Based Semi-Autonomous Control Architecture for Robotic Exploration of Search and Rescue Environments.
300 $a 92 p.
500 $a Source: Masters Abstracts International, Volume: 50-05, page: .
500 $a Adviser: Goldie Nejat.
502 $a Thesis (M.A.Sc.)--University of Toronto (Canada), 2011.
520 $a Semi-autonomous control schemes can address the limitations of both teleoperation and fully autonomous robotic control of rescue robots in disaster environments by allowing cooperation and task sharing between a human operator and a robot with respect to tasks such as navigation, exploration and victim identification. Herein, a unique hierarchical reinforcement learning (HRL) -based semi-autonomous control architecture is presented for rescue robots operating in unknown and cluttered urban search and rescue (USAR) environments. The aim of the controller is to allow a rescue robot to continuously learn from its own experiences in an environment in order to improve its overall performance in exploration of unknown disaster scenes. A new direction-based exploration technique and a rubble pile categorization technique are integrated into the control architecture for exploration of unknown rubble filled environments. Both simulations and physical experiments in USAR-like environments verify the robustness of the proposed control architecture.
590 $a School code: 0779.
650 4 $a Engineering, Mechanical. $3 845387
650 4 $a Engineering, Robotics. $3 845425
690 $a 0548
690 $a 0771
710 2 $a University of Toronto (Canada). $b Mechanical and Industrial Engineering. $3 845502
773 0 $t Masters Abstracts International $g 50-05.
790 1 0 $a Nejat, Goldie, $e advisor
790 $a 0779
791 $a M.A.Sc.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR76521

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