| Abstrakt: |
With the rapid advancement of manufacturing in China, robot machining technology has become a popular research subject. An increasing number of robots are currently being used to perform complex tasks during manual operation, e.g., the grinding of large components using multi-robot systems and robot teleoperation in dangerous environments, and machining conditions have evolved from a single open mode to a multisystem closed mode. Because the environment is constantly changing with multiple systems interacting with each other, traditional methods, such as mechanism modeling and programming are no longer applicable. Intelligent learning models, such as deep learning, transfer learning, reinforcement learning, and imitation learning, have been widely used; thus, skill learning and strategy optimization have become the focus of research on robot machining. Skill learning in robot machining can use robotic flexibility to learn skills under unknown working conditions, and machining strategy research can optimize processing quality under complex working conditions. Additionally, skill learning and strategy optimization combined with an intelligent learning model demonstrate excellent performance for data characteristics learning, multisystem transformation, and environment perception, thus compensating for the shortcomings of the traditional research field. This paper summarizes the state-of-the-art in skill learning and strategy optimization research from the perspectives of feature processing, skill learning, strategy, and model optimization of robot grinding and polishing, in which deep learning, transfer learning, reinforcement learning, and imitation learning models are integrated into skill learning and strategy optimization during robot grinding and polishing. Finally, this paper describes future development trends in skill learning and strategy optimization based on an intelligent learning model in the system knowledge transfer and nonstructural environment autonomous processing. [ABSTRACT FROM AUTHOR] |
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