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. / In general, it is cast as a dynamic resource allocation problem. Given a set of resources and a sequence of agents, the goal is to distribute resources to agents in an optimal manner. The resource allocation problem is an NP-hard problem in general. This thesis strives to minimize the resource usage and task completion time by scheduling a number of requests from robots. However, actual realization of fully distributed cloud robotic systems is rarely found in the community. Moreover, unconstrained resources in the cloud are not commonly implemented. Therefore, the optimization of autonomously implemented resource allocation is the primary focus of the thesis. / 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. / 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. / 雲機器人是利用雲計算作為龐大的資源池進行大規模并行計算和資源共用。此外，雲機器人系統避免了用於維護和更新各個機器人客戶端的開銷，並降低了機器人客戶端對中間件的依賴。這對於服務機器人尤其有益，因為大量計算所需要的能量會減少機器人運行的持續時間，並且約束機器人移動性能以及增加機器人的成本。爲了更好的利用雲技術提高機器人的服務性能，最重要的是允許不同類型的機器人共用資源，尤其是多傳感器的信息，並在雲上開發新的功能和新的應用。 / 此類問題一般被規劃為動態資源配置問題，即給定一個資源集合和一個多客戶端的序列，最優化地進行資源的分配。資源配置問題是一個非確定性多項式複雜 (NP-hard) 問題 。本文通過優化調度多個資源請求，力求最大限度地減少資源的使用和任務完成的時間。目前很少有真正實現了完全分布式的雲機器人系統。此外，在實際的雲系統中並不存在無限的不受約束的資源。因此，自主地優化雲機器人系統的資源配置是雲機器人系統的關鍵問題，有著重要的現實意義。 / 雖然在資源配置和任務分配領域已經有大量的研究工作，當這兩者在典型的雲機器人系統中結合時，仍然有大量新的問題需要研究。本文著重於以下幾個方面：首先，建立多傳感器資料的檢索架構，即基於twisted的socket 框架建立任務分配和服務合同的構建方法，用於實現多傳感器信息的異步傳輸，同時將其用於研究有效的分布式多機器人協作。其次，提出基於市場范式的調度機制，用於解決雲計算機器人系統的動態資源配置問題；並針對一系列服務品質指標進行優化和驗證，特別是實現回應時間的最小化，以滿足機器人任務即時性的要求。第三，提出基於鏈路信號強度矩陣的協商策略以減輕在移動自組網路中多個機器人的通信競爭；此外，考慮到多跳、時延和鏈路品質等問題，本文提出了增量式的拍賣算法策略；當移動自組網中存在多機器人系統時，所涉算法對無人干擾情況下的公平分配和當有使用者有偏好情況下的偏好分配分別進行了優化。通過解決以上問題，本文的貢獻有助於通用的雲機器人系統融入到人類日常生活和工作中。 / 未來的研究將側重于面向機器人任務分配的問題，例如監控，多機器人嚮導等，及其他受益于雲機器人平臺的各類應用解決方案。 / Wang, Lujia. / Thesis Ph.D. Chinese University of Hong Kong 2014. / Includes bibliographical references (leaves 115-127). / Abstracts also in Chinese. / Title from PDF title page (viewed on 07, October, 2016). / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only.