## 2 Related Work  
Cloud robot services applying the technology of cloud computing to robots utilize computation, storage, and communication in the internet infrastructure. For instance, RoboEarth, a pioneering robot cloud service, supports the service of storing software components, mapping information, behavior information, and object information in a database (DB) and utilizing a cloud engine for services; robots can use it by virtualizing the information [1]. However, there have been different opinions regarding the usefulness of robot clouds. An advocative side, on one hand, agrees that a robot can enhance its capability by combining it with robot cloud services. [PERSON] insists that the robot cloud, for a robot system having limited computing power, can carry over-burdened tasks to cloud servers [10]. On the other hand, [PERSON] noted that the approach of robot clouds could result in harmful effects to the performance of robots [11]. He insisted that the rapid advancement of real-time capability of on-board processing could make the concept of cloud robotics meaningless, and the cloud robot depending on the network could weaken the autonomy and reliability of robots. Nevertheless, a robot cloud has characteristics of cloud computing and robot technology and the unique characteristics of the robot cloud itself. Particularly at the robotics level, robots in the robot cloud can communicate with each other to share the burden of tasks rather than serve as isolated devices that allow only data exchange with remote servers. As a result, the robot cloud will optimize the outstanding achievements that exist in robotics. There are different levels of service in the field of cloud computing comprising infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) [12]. These models can be applied to robot clouds and multiple studies regarding them have been introduced. [PERSON] et al. [13] highlighted aspects of IaaS in robotic applications. They proposed an architecture that enables cost-effectiveness by delegating virtualization and dynamic tasks to robots, including robots that could belong to other clouds. [PERSON] et al. [14] proposed a PaaS approach to a configurable suite of products based on cloud robotics applications. It allowed end users to be free from the low-level decisions needed to construct architectures of complex systems distributed among robots and clouds. For example, if the REALabs platform was built using a PaaS model, it was predicted that many robot applications could be developed in this area [15]. In the framework of SaaS, a robot used a remote server for the training of locations with a neural network [2]. This case was used to establish communication between the cloud and the robot through a wide environment and to identify its location in images transmitted by the robot at the SaaS level. [PERSON] and [PERSON] [16] described robot as a service (RaaS) with the Internet of Things. The RaaS was able to create a local pool of intelligent devices using autonomous and intelligent robots and make local decisions without communicating with the cloud. However, networking robots have given rise to security and latency issues. [PERSON] et al. [PERSON] et al. (2017) determined that a number of robots are accessible and controllable from the Internet and dangerous to both the robot and human, and the robot's sensors can be viewed as a threat to privacy. [PERSON] et al. [PERSON] et al. (2018) issued that cloud robotics that come with a key communicating with the cloud over congested wireless networks may result in latency or loss of data, and formulated offloading as a sequential decision-making problem, and proposed a solution using deep reinforcement learning. To overcome security and latency matters of cloud robotics and fog robotics, a new network model was introduced [PERSON] et al. (2018). The model, as shown in Figure 1, distributes storage, computer, and networking resources between the cloud and the edge in a federate manner. In the work of [PERSON] et al.