Abstract
The application scenarios and market shares of industrial robots have been increasing in recent years, and with them comes a huge market and technical demand for industrial robot-monitoring system (IRMS). With the development of IoT and cloud computing technologies, industrial robot monitoring has entered the cloud computing era. However, the data of industrial robot-monitoring tasks have characteristics of large data volume and high information redundancy, and need to occupy a large amount of communication bandwidth in cloud computing architecture, so cloud-based IRMS has gradually become unable to meet its performance and cost requirements. Therefore, this work constructs edge–cloud architecture for the IRMS. The industrial robot-monitoring task will be executed in the form of workflow and the local monitor will allocate computing resources for the subtasks of the workflow by analyzing the current situation of the edge–cloud network. In this work, the allocation problem of industrial robot-monitoring workflow is modeled as a latency and cost bi-objective optimization problem, and its solution is based on the evolutionary algorithm of the heuristic improvement NSGA-II. The experimental results demonstrate that the proposed algorithm can find non-dominated solutions faster and be closer to the Pareto frontier of the problem. The monitor can select an effective solution in the Pareto frontier to meet the needs of the monitoring task.
Highlights
The use of industrial robots in manufacturing industry is increasing rapidly [1], and industrial robot-monitoring systems (IRMS) have played an important role in maintaining the normal operation of industrial robots and even the whole factory, most of the IRMSs are based on B/S or C/S architecture remote monitoring by the Internet [2]
Hanbo Yang et al [3] implemented a cloud manufacturing monitoring platform based on 5G and SIM (Standard Information Model), Rachmad Andri Atmoko et al [4] implemented a cloud monitoring industrial arm robot based on MQTT protocol
The performance of INSGA2-TGHR algorithm is stable on different types and number of computing resources sets, sensitive to the number of instructions of the tasks, and able to offload computationally intensive tasks to more clouds
Summary
The use of industrial robots in manufacturing industry is increasing rapidly [1], and industrial robot-monitoring systems (IRMS) have played an important role in maintaining the normal operation of industrial robots and even the whole factory, most of the IRMSs are based on B/S or C/S architecture remote monitoring by the Internet [2]. With the development of IoT and cloud computing technology, IRMSs based on cloud computing architecture have emerged. Hanbo Yang et al [3] implemented a cloud manufacturing monitoring platform based on 5G and SIM (Standard Information Model), Rachmad Andri Atmoko et al [4] implemented a cloud monitoring industrial arm robot based on MQTT protocol. Cloud robotics has become an emerging area of robotics research [5], where the technological key is computational offloading, when the robot controller generates compute-intensive tasks to the cloud in order to reduce the requirements for controller performance and the computational energy consumption of the robot. Wan [8] offload computationally intensive tasks such as robot grasping, simultaneous localization and mapping (SLAM), and navigation of cloud robots to the cloud
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