原题目：Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey
Abstract
Abstract—Robotic applications nowadays are widely adopted to enhance operational automation and performance of real-world CyberPhysical 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 robotto-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.
Introduction
在多信息物理系统的条件下，多智能体云机器人可以带来以下优点：
- 通过利用不同基础设施级别的计算资源，它为 CPS 提供执行各种机器人应用程序的选项。
- 它提供了一种抽象，可以根据计算基础设施的特征对机器人应用程序的任务进行分类。
- 它提供分布式资源来处理CPS中生成的大数据，并减少通信延迟。
- 它增强了机器人使用本地和边缘网络共享知识的能力，而不是依赖于远程云。
- 通过使资源更接近机器人，它显着降低了处理多个设备的 CPS 中的带宽要求（用于将数据发送到云）。
- 通过多智能体云机器人技术，CPS 对云数据中心的依赖性以及云数据中心（处理多个 CPS）的负载急剧降低。
多智能体云机器人系统和相关的计算范例
接下来，将多智能体云机器人技术同移动云计算（MCC）和多接入边缘计算（MEC）进行了对比。相较于MCC和MEC，多智能体云机器人技术的网络连接方式更多样，而且支持AI智能决策，这使其更复杂。因此，有效的资源调度很重要，尤其是在这种需求多样的条件下。
多智能体云机器人系统的资源调度和服务提供的挑战
- 实时学习和自动的动作
- 复杂的数据流处理
- 动态的机器人合作
- 跨基础设施的互操作性
- 异步的决策
- 按需计算和通信权衡
- 系统的特定策略
- 能源延迟优化
- 综合商业模式
- 异构导致的安全问题
文章的贡献
- 在考虑资源类型，表现评价计量，应用结构，服务模型和分发机制的情况下对资源调度进行了分类。
- 将用于有效提供服务的资源池化，计算迁移和任务调度的现有方法进行了很好的探索。
- 从研究综述中学到的知识进行聚合，并且填补了在多智能体云机器人系统中机器人系统的资源调度和服务提供的gap。
- 探索了未来的研究方向。