The atmospheric infrared sounder (AIRS) provides a robust and accurate data source to investigate the variability of mid-tropospheric CO2 globally. In this paper, we use the AIRS CO2 product and other auxiliary data to survey the spatiotemporal distribution characteristics of mid-tropospheric CO2 and the controlling factors using linear regression, empirical orthogonal functions (EOFs), geostatistical analysis, and correlation analysis. The results show that areas with low mid-tropospheric CO2 concentrations (20°S–5°N) (384.2 ppm) are formed as a result of subsidence in the atmosphere, the presence of the Amazon rainforest, and the lack of high CO2 emission areas. The areas with high mid-tropospheric CO2 concentrations (30°N–70°N) (382.1 ppm) are formed due to high CO2 emissions. The global mid-tropospheric CO2 concentrations increased gradually (the annual average rate of increase in CO2 concentration is 2.11 ppm/a), with the highest concentration occurring in spring (384.0 ppm) and the lowest value in winter (382.5 ppm). The amplitude of the seasonal variation retrieved from AIRS (average: 1.38 ppm) is consistent with that of comprehensive observation network for trace gases (CONTRAIL), but smaller than the surface ground stations, which is related to altitude and coverage. These results contribute to a comprehensive understanding of the spatiotemporal distribution of mid-tropospheric CO2 and related mechanisms.
In order to solve the problem of demand for large capacity storage and high-performance computing resource of intelligent robot navigation and demand-service matching in the field of assisting handicapped or elderly people, overcoming the limitations of carrying resources, the method of constructing robot service platform for assisting handicapped or elderly people (RSP-AHEP) was proposed based on cloud robotics technology. Firstly, the demand of assisting handicapped or elderly people and the corresponding robot service category was analyzed to assure relationship-matching subjects. Secondly, based on VMware and Hadoop cluster technology, the architecture of a three-layer robot service platform was designed, which were universal interface layer, resource service layer, and application layer. Complex computing tasks, such as the matching computing between robot service and demand of the handicapped or elderly people, and the robot path-planning service, were placed in the robot service platform with the advantages of storage and computing resources. Thirdly, the remote communication between the robot and the service platform was realized based on the ROS (Robot Operation System) technology; finally, the function experiments, which included the remote dispatch, path planning, and service response between the service platform and robot, were carried out in the simulation environment. The result verified the feasibility of the proposed method.
Delivery robots face the problem of storage and computational stress when performing immediate tasks, exceeding the limits of on-board computing power. Based on cloud computing, robots can offload intensive tasks to the cloud and acquire massive data resources. With its distributed cluster architecture, the platform can help offload computing and improve the computing power of the control center, which can be considered the external “brain” of the robot. Although it expands the capabilities of the robot, cloud service deployment remains complex because most current cloud robot applications are based on monolithic architectures. Some scholars have proposed developing robot applications through the microservice development paradigm, but there is currently no unified microservice-based robot cloud platform. This paper proposes a delivery robot cloud platform based on microservice, providing dedicated services for autonomous driving of delivery robot. The microservice architecture is adopted to split the monomer robot application into multiple services and then implement automatic orchestration and deployment of services on the cloud platform based on components such as Kubernetes, Docker, and Jenkins. This enables containerized CI/CD (continuous integration, continuous deployment, and continuous delivery) for the cloud platform service, and the whole process can be visualized, repeatable, and traceable. The platform is prebuilt with development tools, and robot application developers can use these tools to develop in the cloud, without the need for any customization in the background, to achieve the rapid deployment and launch of robot cloud service. Through the cloud migration of traditional robot applications and the development of new APPs, the platform service capabilities are continuously improved. This paper verifies the feasibility of the platform architecture through the delivery scene experiment.
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