Cloud robot platforms with different types of data storage and transmission modes

Abstract

This invention provides a cloud robot platform with different data storage and transmission modes, characterized by comprising, from bottom to top, a service robot layer, a network communication layer, a cloud platform layer, and an application layer. The system provided by this invention combines the advantages of robotics and cloud computing, along with the unique features of an intelligent robot cloud. Robots within the system can communicate with each other and exchange data with a remote cloud server. Autonomous robot agents within the system can communicate and collaborate with cloud computing infrastructure, resolving issues related to asynchronous communication and compatibility among multiple robot systems. Standardizing the interfaces for accessing and storing data in the robot system helps address benchmarking in robotics. This invention offers a wider range of task domains, better robustness, lower economic costs, and higher efficiency.

Description

Technical Field

[0001] This invention relates to a cloud robot platform. Background Technology

[0002] Current cloud robotics platforms can be viewed as "cloud + robot." The "cloud," as the software infrastructure of the cloud robotics platform, refers to the cloud robotics environment with a cloud data center. The "robot" is the hardware infrastructure of the cloud robotics platform. To implement the aforementioned cloud robotics platform, an autonomous, low-maintenance cloud infrastructure is designed, providing resources based on supply and demand. Simultaneously, an intelligent engine is created within the cloud infrastructure to perform real-time resource scheduling and management for the robots, thereby simplifying the robots' hardware. Robots share knowledge through the cloud, ultimately forming the cloud robotics platform.

[0003] This cloud robot platform moves most of the complex computing and storage tasks to the cloud, but does not enhance the capabilities of the cloud robots themselves. Therefore, the robots in the robot layer of the cloud robot platform are relatively isolated, which reduces the autonomy and reliability of the cloud robot platform.

[0004] Regarding robot hardware, different manufacturers' equipment and sensors can result in varying output data structures. Even different models from the same manufacturer may output data with different structures, leading to significant differences in output data structures between different manufacturers or even different models from the same manufacturer. The storage and transmission methods for request / response data (such as video, music, and text) generated by different types of users on cloud platforms also vary considerably. To address this issue, mainstream cloud platforms currently offer multiple interfaces for various data formats. However, the limited number of interfaces presents challenges in data format conversion when handling uploads and downloads. Appropriate preprocessing is necessary when uploading data to the cloud platform, and the robustness and real-time performance of data exchange significantly impact the conversion from one data format to another. Furthermore, current cloud robot platforms are hampered by difficulties in interacting with different types and formats of data, primarily focusing on all robots executing the same instructions and tasks, which is insufficient in the field of multi-robot collaboration. Summary of the Invention

[0005] The technical problem that this invention aims to solve is that current cloud robot platforms are hampered by difficulties in interacting with different types and formats of data. They mainly rely on all robots executing the same instructions and tasks, which is a significant shortcoming in the field of multi-robot collaboration.

[0006] To address the aforementioned technical problems, the present invention provides a cloud robot platform with different types of data storage and transmission modes, characterized in that it comprises, from bottom to top, a service robot layer, a network communication layer, a cloud platform layer, and an application layer, wherein:

[0007] The service robot layer consists of multiple robots. Each robot includes robot hardware and robot software running on the robot hardware. The various types of data generated by the robot hardware are uniformly packaged into a standard format by the robot software.

[0008] The network communication layer is the foundation for interaction and collaboration between robots in the service robot layer, as well as for interaction and collaboration between the service robot layer and the cloud platform layer. The network communication layer is a layered structure with a lower layer, a mapping layer, and an upper layer. The lower layer provides standard data interfaces downwards; data between the lower and upper layers is mapped through the mapping layer; and the upper layer facilitates data interaction upwards.

[0009] The cloud platform layer provides a large amount of data information to the robots in the service robot layer, and all the calculations, fusion analysis, behavior control and resource scheduling of each robot in the service robot layer are performed in the cloud platform layer. The cloud platform layer flexibly allocates cloud resources according to the real-time needs of the robots and uses cloud infrastructure to continuously simplify the hardware facilities and control of each robot in the service robot layer.

[0010] The application layer runs on the cloud platform layer, which uses the cloud platform layer as a type of cloud computing platform to provide cloud services similar to cloud computing to the robots in the service robot layer.

[0011] Preferably, the robot software is the robot operating system ROS, and all types of data generated by the robot hardware are uniformly packaged into the standard ROS format.

[0012] Preferably, the mapping layer is variable, and the robot management configuration can be dynamically modified in the relevant configuration file, thereby enabling flexible management of cloud robots in the robot cloud system.

[0013] Preferably, based on the different types of cloud resources used by each robot in the service robot layer during task execution, the cloud platform layer is divided into:

[0014] The cloud platform layer based on the remote cloud model: The cloud platform layer is implemented based on remote cloud resources, and each robot in the service robot layer accesses remote cloud resources through the network communication layer via a wide area network.

[0015] The cloud platform layer based on the local cloud model: The cloud platform layer is implemented based on local cloud resources, and each robot in the service robot layer accesses local cloud resources through the local network via the network communication layer.

[0016] The cloud platform layer based on the self-organizing model of robots consists of cloud computing resources formed by the self-organization of multiple robots in the service robot layer. The cloud platform layer is implemented based on these cloud computing resources and provides cloud computing services to the robots themselves or other terminal devices through the network communication layer.

[0017] Preferably, the cloud services provided by the application layer include:

[0018] Hardware services: Robots in the service robot layer offload computationally intensive tasks to the cloud platform layer through hardware services provided by the application layer, reducing the processing burden on the service robots themselves.

[0019] Database service: Robots in the service robot layer upload resource information to the cloud infrastructure data center through the database service, enabling multiple objects to share a virtual resource pool;

[0020] Communication services: Robots in the service robot layer interact with the cloud platform layer in real time as needed through communication services, enabling better collaboration;

[0021] Monitoring services: The cloud platform layer uses monitoring services to monitor and measure robot objects in the service robot layer in real time, and enables rapid redeployment based on virtualization technology to improve flexibility;

[0022] Attach service: Each user application has its own functions, and they can access services provided for the current user application in the cloud platform layer from time to time through the attach service;

[0023] Advanced Application Services: Robots in the service robot layer implement advanced applications through advanced application services.

[0024] Preferably, when the cloud platform layer is a cloud platform layer based on the self-organizing mode of robots, the robots in the service robot layer offload computationally intensive tasks to other robots that self-organize to form cloud computing resources through hardware services provided by the application layer.

[0025] Preferably, in the data mode of the cloud robot platform, multiple robots in the service robot layer can achieve multi-robot collaborative work, wherein the process of multi-robot collaboration is regarded as a collaborative computing network.

[0026] Preferably, when multiple robots work collaboratively in the cloud robot platform, the cloud robot platform achieves large-scale, fine-grained perception and performs network data transmission through a swarm intelligence sensing network composed of mobile devices.

[0027] Preferably, the swarm intelligence sensing network is divided into three modes: centralized sensing, distributed sensing, and hybrid sensing.

[0028] This invention proposes an "intelligent robot cloud" structure based on a low-interaction-cost data model to bridge the power of robotics and cloud computing. The system provided by this invention combines the advantages of both robotics and cloud computing with the unique characteristics of an intelligent robot cloud. Robots within the system can communicate with each other and exchange data with remote cloud servers. Autonomous robot agents within the system can communicate and collaborate with cloud computing infrastructure, resolving issues related to asynchronous communication and compatibility among multiple robot systems. Standardizing the interfaces for accessing and storing data in the robot system helps address benchmarking in robotics. Unique features such as remote control, big data and shared knowledge bases, collective learning, and intelligent behavior allow multiple robots to form a self-organizing cloud environment without a cloud data center. Compared to existing cloud robot platforms, this invention offers a wider range of task domains, better robustness, lower economic costs, and higher efficiency. Attached Figure Description

[0029] Figure 1 The system structure of the cloud robot platform proposed in this invention is illustrated.

[0030] Figure 2 This illustrates the application scenarios of the present invention. Detailed Implementation

[0031] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined by the appended claims.

[0032] Combination Figure 1 and Figure 2 The system architecture of the cloud robot platform with different types of data storage and transmission modes provided by this invention is mainly based on the design concept of Robots as a Service. On the one hand, the local resources of the cloud robot platform are configured as cloud services for users to directly call; on the other hand, cloud resources can also be utilized for user use.

[0033] The present invention mainly comprises, from bottom to top, a service robot layer, a network communication layer, a cloud platform layer, and an application layer.

[0034] The service robot layer consists of multiple robots, each comprising robot hardware and robot software running on that hardware. The robot software for all robots includes the Robot Operating System (ROS). All types of data generated by the robot hardware (such as data from various sensors) are uniformly encapsulated in the standard ROS format, providing a unified interface for data interaction within the service robot layer.

[0035] The network communication layer is the foundation for interaction and collaboration between robots within the service robot layer, as well as between the service robot layer and the cloud platform layer. The network communication layer is a layered structure with a lower layer, a mapping layer, and an upper layer. The lower layer provides standard data interfaces downwards; data between the lower and upper layers is mapped through the mapping layer. The upper layer facilitates data interaction upwards and does not need to concern itself with how the lower layer executes tasks. Furthermore, the mapping layer is variable; robot management configurations can be dynamically modified in relevant configuration files, enabling flexible management of cloud robots within the robot cloud system.

[0036] The communication layer, which forms the foundation for interaction and collaboration between cloud robots, has a layered structure with a mapping layer. The lower layer provides standard interfaces, and the higher layers do not need to concern themselves with how the lower layer executes tasks. Furthermore, this mapping layer is variable; robot management can be dynamically modified in relevant configuration files, thus enabling flexible management of robots within the service robot layer.

[0037] The cloud platform layer can be considered the external "brain" of each robot in the service robot layer. It provides robots with a large amount of data, and all computations, fusion analysis, behavior control, and resource scheduling for each robot in the service robot layer are performed at the cloud platform layer. The cloud platform layer elastically allocates cloud resources according to the real-time needs of the cloud robots, continuously simplifying the hardware and control of each robot in the service robot layer by utilizing cloud infrastructure. Based on the different types of cloud resources used by each robot in the service robot layer during task execution, the cloud platform layer can be further divided into:

[0038] The cloud platform layer based on the remote cloud model: The cloud platform layer is implemented based on remote cloud resources, and each robot in the service robot layer accesses remote cloud resources through the network communication layer via a wide area network.

[0039] The cloud platform layer based on the local cloud model: The cloud platform layer is implemented based on local cloud resources, and each robot in the service robot layer accesses local cloud resources through the local network via the network communication layer.

[0040] The cloud platform layer based on the self-organizing model of robots consists of cloud computing resources formed by the self-organization of multiple robots in the service robot layer. The cloud platform layer is implemented based on these cloud computing resources and provides cloud computing services to the robots themselves or other terminal devices through the network communication layer.

[0041] The application layer runs on the cloud platform layer, which uses the cloud platform layer as a type of cloud computing platform to provide cloud services similar to cloud computing to the robots in the service robot layer, including:

[0042] Hardware services: Robots in the service robot layer offload computationally intensive tasks to the cloud platform layer through hardware services provided by the application layer (when the cloud platform layer is a cloud platform layer based on the self-organizing mode of robots, the tasks are offloaded to other robots that self-organize to form cloud computing resources), thereby reducing the processing burden on the service robots themselves.

[0043] Database service: Robots in the service robot layer upload resource information to the cloud infrastructure data center through the database service, enabling multiple objects to share a virtual resource pool.

[0044] Communication services: Robots in the service robot layer interact with the cloud platform layer in real time as needed through communication services, enabling better collaboration.

[0045] Monitoring services: The cloud platform layer uses monitoring services to monitor and measure robot objects in the service robot layer in real time, and enables rapid redeployment based on virtualization technology to improve flexibility.

[0046] Attached services: Each user application has its own functions, and they can access services provided for the current user application in the cloud platform layer from time to time through attached services to enhance their functions.

[0047] Advanced Application Services: Robots in the service robot layer can achieve advanced applications such as autonomous learning, knowledge sharing (cloud robots teaching cloud robots), big data mining, speech recognition, SLAM, navigation, and grasping through advanced application services.

[0048] Under the data model of the cloud robot platform mentioned above, multiple robots in the service robot layer can achieve multi-robot collaborative work. The process of multi-robot collaboration is regarded as a collaborative computing network. The main advantages of the collaborative computing network are: (1) A collaborative computing network can collect, compute and store resources, and can dynamically allocate these resources according to specific work requirements; (2) Due to the exchange of information, machines can make collaborative work decisions.

[0049] Collaborative computing networks greatly expand the complexity of tasks that cloud robotics platforms can manage and improve the efficiency of specific tasks.

[0050] Meanwhile, due to the ubiquitous use, wide distribution, and natural mobility of mobile devices, they can form a swarm intelligence sensing network. Multi-robot collaborative work in a cloud robotics platform can achieve large-scale, fine-grained sensing and data transmission through this swarm intelligence sensing network. The swarm intelligence sensing network in a cloud robotics platform can be categorized into three modes: centralized sensing, distributed sensing, and hybrid sensing.

Claims

1. A cloud robot platform with different types of data storage and transmission modes, characterized in that, From bottom to top, the system includes a service robot layer, a network communication layer, a cloud platform layer, and an application layer, wherein: The service robot layer is composed of multiple robots, each of which includes robot hardware and robot software running on the robot hardware, and various types of data generated by the robot hardware are uniformly encapsulated into a standard format by the robot software; The network communication layer is the basis for interaction and cooperation among the robots in the service robot layer and for interaction and cooperation between the service robot layer and the cloud platform layer; the network communication layer is a layered structure with a lower layer structure, a mapping layer, and an upper layer structure; the lower layer structure is used to provide a standard data interface downward, the data of the lower layer structure and the upper layer structure are mapped to each other through the mapping layer, and the upper layer structure is used to realize data interaction upward; The cloud platform layer provides a large amount of data information for the robots in the service robot layer, and all the computing, fusion analysis, behavior control, and resource scheduling of the robots in the service robot layer are performed in the cloud platform layer; the cloud platform layer elastically allocates cloud resources according to the real-time needs of the robots, and uses cloud infrastructure to continuously simplify the hardware facilities of the robots in the service robot layer and control the robots; The application layer runs on the cloud platform layer, and uses the cloud platform layer as a type of cloud computing platform to provide cloud services similar to cloud computing for the robots in the service robot layer; The cloud services provided by the application layer include: Hardware service: the robots in the service robot layer offload computationally intensive tasks to the cloud platform layer through the hardware service provided by the application layer, reducing the processing burden of the service robots themselves; Database service: the robots in the service robot layer upload resource information to the cloud infrastructure data center through the database service, realizing a multi-object shared virtual resource pool; Communication service: the robots in the service robot layer interact with the cloud platform layer in real time as needed through the communication service, better performing cooperation; Monitoring service: the cloud platform layer monitors and measures the robot objects in the service robot layer in real time through the monitoring service, and realizes rapid redeployment based on virtualization technology, improving flexibility; Attachment service: each user application has certain functions, and they can access the services provided by the cloud platform layer for the current user application through the attachment service from time to time; Advanced application service: the robots in the service robot layer realize advanced applications through the advanced application service.

2. A cloud robot platform having different types of data storage and transmission modes as claimed in claim 1, wherein, If the robot software is a robot operating system (ROS), the various types of data generated by the robot hardware are uniformly encapsulated into a standard ROS format.

3. A cloud robot platform having different types of data storage and transmission modes as claimed in claim 1, wherein, The mapping layer is variable, and the robot management configuration is dynamically modified in a related configuration file, thereby flexibly managing the cloud robots in the robot cloud system.

4. A cloud robot platform having different types of data storage and transmission modes as claimed in claim 1, wherein, According to the different types of cloud resources used by the robots in the service robot layer in the process of executing tasks, the cloud platform layer is divided into: A cloud platform layer based on a remote cloud mode: the cloud platform layer is implemented based on remote cloud resources, and the robots in the service robot layer access the remote cloud resources through the network communication layer via a wide area network; The cloud platform layer based on a local cloud mode: the cloud platform layer is implemented based on local cloud resources, and each robot in the service robot layer accesses the local cloud resources through the network communication layer via a local network; The cloud platform layer based on a robot self-organization mode: the cloud computing resources are self-organized by a plurality of robots in the service robot layer, the cloud platform layer is implemented based on the cloud computing resources, and the cloud platform layer provides cloud computing services for the robots themselves or other terminal devices via the network communication layer.

5. A cloud robot platform having different types of data storage and transmission modes as claimed in claim 1, wherein, When the cloud platform layer is the cloud platform layer based on the robot self-organization mode, the robots in the service robot layer offload the computationally intensive tasks to other robots self-organized to form the cloud computing resources through the hardware services provided by the application layer.

6. A cloud robot platform having different types of data storage and transmission modes as claimed in claim 1, wherein, In the data mode of the cloud robot platform, a plurality of robots in the service robot layer can implement multi-robot cooperative work, wherein the process of multi-robot cooperation is regarded as a cooperative computing network.

7. A cloud robot platform having different types of data storage and transmission modes as claimed in claim 6, wherein, When the multi-robot cooperative work in the cloud robot platform, the cloud robot platform implements large-scale and fine-grained perception through a crowd sensing network composed of mobile devices, and performs network data transmission.

8. A cloud robot platform having different types of data storage and transmission modes as claimed in claim 7, wherein, The crowd sensing network is divided into three modes: centralized sensing, distributed sensing and hybrid sensing.

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