Multi-granularity service composition in industrial cloud robotics
Industrial cloud robotics employs cloud computing technology to provide various operational services, such as robotic control modules that enable customized screwing and welding. Service composition technology enables the flexible implementation of complex industrial robotic applications based on the collaboration of multiple industrial cloud robotic services. Most studies considered cloud robotic services with a single robotic manipulator with a fixed function. To utilize the advantages of coarse-grained services encapsulated by multi-functional robots, manipulators, and control applications, a multi-granularity service composition method is introduced considering the multi-functional resources and capabilities of the cloud robotic services. Then a quality-of-service-aware multi-granularity robotic service composition model is built to evaluate the composition solution. Furthermore, a multi-granularity robotic service matching strategy is proposed according to the matching constraints of coarse-grained services. Six representative multi-objective evolutionary algorithms are adopted to optimize five quality-of-service attributes of the composite service simultaneously. Experiments demonstrate that the proposed multi-granularity robotic service composition method can remarkably improve the quality of robotic composite services for complex manufacturing tasks by utilizing coarse-grained services in addition to fine-grained services. The performances of six multi-objective evolutionary algorithms are compared to determine the most suitable algorithm for the multi-granularity robotic service composition problem.