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A Study on Resource Allocation Strat...

Wang, Lujia.

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A Study on Resource Allocation Strategies for Cloud Robotic Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A Study on Resource Allocation Strategies for Cloud Robotic Systems./
作者:	Wang, Lujia.
面頁冊數:	150 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-01(E), Section: B.
Contained By:	Dissertation Abstracts International77-01B(E).
標題:	Robotics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3725363
ISBN:	9781339096087

A Study on Resource Allocation Strategies for Cloud Robotic Systems.
Wang, Lujia.

A Study on Resource Allocation Strategies for Cloud Robotic Systems. - 150 p.

Source: Dissertation Abstracts International, Volume: 77-01(E), Section: B.

Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2014.

The new approach of cloud robotics takes advantage of cloud computing as a vast resource pool for massively parallel computation and sharing of data. Besides, the cloud robotic system removes overheads for maintenance and updates, and reduces dependence on user middleware. This is of particular interest for service robots, because on-board computation entails additional power requirements which may reduce operating duration and constrain robot mobility as well as costs. In order to utilize the cloud technology in service robots, it is crucial to allow different types of robots to share information and to develop new skills on the cloud.

ISBN: 9781339096087Subjects--Topical Terms:

519753
Robotics.

A Study on Resource Allocation Strategies for Cloud Robotic Systems.
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502 $a Thesis (Ph.D.)--The Chinese University of Hong Kong (Hong Kong), 2014.
520 $a The new approach of cloud robotics takes advantage of cloud computing as a vast resource pool for massively parallel computation and sharing of data. Besides, the cloud robotic system removes overheads for maintenance and updates, and reduces dependence on user middleware. This is of particular interest for service robots, because on-board computation entails additional power requirements which may reduce operating duration and constrain robot mobility as well as costs. In order to utilize the cloud technology in service robots, it is crucial to allow different types of robots to share information and to develop new skills on the cloud.
520 $a The new approach of cloud robotics takes advantage of cloud computing as a vast resource pool for massively parallel computation and sharing of data. Besides, the cloud robotic system removes overheads for maintenance and updates, and reduces dependence on user middleware. This is of particular interest for service robots, because on-board computation entails additional power requirements which may reduce operating duration and constrain robot mobility as well as costs. In order to utilize the cloud technology in service robots, it is crucial to allow different types of robots to share information and to develop new skills on the cloud.
520 $a While a respectable amount of work is done on both resource and task allocation, there is still the need for research towards the integration of problems in a typical cloud robotic system. For the outlined difficulties, this thesis addresses novel research on the following aspects: At first, the underlying architecture of Multi Sensor Data Retrieval (MSDR) is implemented on the twisted-based socket for asynchronous data transmission, which is also investigated as effective decentralized methods for multi-robot coordination, task assignment and service contract establishing. Second, the market-based scheduling mechanism is proposed for the dynamic resource allocation problem in cloud robotics. A set of criteria as empirical Quality of Service (QoS) is optimized, especially Time to Response (ToR) is minimized to fulfill Firm Real-Time (FRT) requirement of robotic tasks. Third, a Link Quality Matrix (LQM) auction-based negotiation strategy is proposed to relieve the competition among multi-robot systems in Mobile Ad-hoc Networks (MANETs). Besides, an incremental auction-based strategy is proposed considering hops, time delay as well as link quality. Both fair allocation when unmanned interference and biased allocation when users have preferences are optimized among multi-robot systems in MANETs. By tackling all these issues, this thesis contributes to general implementation of cloud robotic system into daily.
520 $a Future research will focus on task-oriented problems, such as smart home surveillance, guiding and etc., which could be better solved benefiting from cloud robotics. Solutions will proposed in a bidirectional way considering both data uploading and downloading.
590 $a School code: 1307.
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650 4 $a Automotive engineering. $3 2181195
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710 2 $a The Chinese University of Hong Kong (Hong Kong). $b Electronic Engineering. $3 2094192
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