Robotic applications nowadays are widely adopted to enhance operational automation and performance of real-world Cyber-Physical Systems (CPSs) including Industry 4.0, agriculture, healthcare, and disaster management. These applications are composed of latency-sensitive, data-heavy, and compute-intensive tasks. The robots, however, are constrained in the computational power and storage capacity. The concept of multi-agent cloud robotics enables robot-to-robot cooperation and creates a complementary environment for the robots in executing large-scale applications with the capability to utilize the edge and cloud resources. However, in such a collaborative environment, the optimal resource allocation for robotic tasks is challenging to achieve. Heterogeneous energy consumption rates and application of execution costs associated with the robots and computing instances make it even more complex. In addition, the data transmission delay between local robots, edge nodes, and cloud data centres adversely affects the real-time interactions and impedes service performance guarantee. Taking all these issues into account, this paper comprehensively surveys the state-of-the-art on resource allocation and service provisioning in multi-agent cloud robotics. The paper presents the application domains of multi-agent cloud robotics through explicit comparison with the contemporary computing paradigms and identifies the specific research challenges. A complete taxonomy on resource allocation is presented for the first time, together with the discussion of resource pooling, computation offloading, and task scheduling for efficient service provisioning. Furthermore, we highlight the research gaps from the learned lessons, and present future directions deemed beneficial to further advance this emerging field.

中文翻译：

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多代理云机器人中的资源分配和服务提供：全面调查

如今，机器人应用已被广泛采用，以增强操作自动化和现实世界中的网络物理系统（CPS）的性能，包括工业4.0，农业，医疗保健和灾难管理。这些应用程序由对延迟敏感，数据量大和计算量大的任务组成。但是，机器人的计算能力和存储容量受到限制。多主体云机器人技术的概念实现了机器人到机器人的协作，并为机器人在执行大规模应用程序时创造了互补的环境，并具有利用边缘和云资源的能力。但是，在这样的协作环境中，要实现机器人任务的最佳资源分配是一项挑战。异质能源消耗率以及与机器人和计算实例相关的执行成本的应用使其变得更加复杂。此外，本地机器人，边缘节点和云数据中心之间的数据传输延迟会对实时交互产生不利影响，并妨碍服务性能的保证。考虑到所有这些问题，本文全面研究了多代理云机器人中资源分配和服务提供的最新技术。本文通过与现代计算范例进行显式比较，展示了多智能体云机器人技术的应用领域，并确定了具体的研究挑战。首次介绍了有关资源分配的完整分类法，并讨论了资源池，计算分流，和任务调度，以实现高效的服务供应。此外，我们强调了从已学到的教训中得出的研究差距，并提出了被认为有利于进一步推进这一新兴领域的未来方向。