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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.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	A Learning-Based Semi-Autonomous Control Architecture for Robotic Exploration of Search and Rescue Environments./
作者:	Doroodgar, Barzin.
面頁冊數:	92 p.
附註:	Source: Masters Abstracts International, Volume: 50-05, page: .
Contained By:	Masters Abstracts International50-05.
標題:	Engineering, Mechanical. -
電子資源:	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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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.
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