Operation monitoring system and monitoring method for online trajectory prediction of industrial robot

Abstract

The invention discloses an operation monitoring system and a monitoring method for online trajectory prediction of an industrial robot. According to the operation monitoring system and the monitoringmethod for online trajectory prediction of an industrial robot, a long-short-term memory network model is established for historical pose data of the industrial robot, and the problems of online trajectory prediction and operation condition monitoring of the industrial robot are focused on; and the established prediction model can effectively solve the problems of nonlinearity and dynamics of operation pose data of the industrial robot, so that an operation state monitoring result is more accurate and reliable, according to the system and the method, the pose coordinates of the industrial robot at the future moment serve as prediction output of the model, long-term and short-term memory network modeling and nonlinear dynamic trajectory prediction are achieved, and monitoring the real-timeoperation condition of the industrial robot is achieved.

Description

technical field [0001] The invention relates to the technical field of industrial robot control, in particular to a long-short-term memory network-based operation monitoring system for online trajectory prediction of industrial robots and a monitoring method thereof. Background technique [0002] With the continuous development of production automation and intelligent technology, the proportion of industrial robots used in manufacturing is increasing. In the application process of industrial robots, how to ensure the reliability and safety of their operation has become a research hotspot in related fields; at present, the monitoring of the operating status of industrial robots is mainly based on data-driven fault detection and fault diagnosis For example, Chen Shijian of South China University of Technology proposed an expert system for fault diagnosis of industrial robots based on fuzzy theory; however, such methods mainly consider the monitoring of the operating status of ...

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Problems solved by technology

In the application process of industrial robots, how to ensure the reliability and safety of their operation has become a research hotspot in related fields; at present, the monitoring of the operating status of industrial robots is mainly based on data-driven fault detection and fault diagnosis For example, Chen Shijian of South China University of Technology proposed an expert system for fault diagnosis of industrial robots based on fuzzy theory; however, such methods mainly consider the monitoring of the operating status of industrial robots themselves, and pay less attention to the real-time status and monitoring of operating trajectories.

[0003] In order to incorporate the online running trajectory into the scope of monitoring the running status of industrial robots, the position and orientation coordinates of the future time can be predicted as the output indicators of the running status of industrial robots, and the reliability of running status monitoring of industrial robots can be improved through online regression prediction; among them, based on data-driven The online regression prediction method is currently the most commonly used partial least squares method (Partial Least Squares, PLS) and its improved method; this type of method uses offline historical data to establish a partial least squares model between general operating variables and quality variables for online operation However, such methods are often based on ideal assumptions such as linear and non-dynamic operating data, or only solve the impact of one of the characteristics on modeling, and it is difficult to solve the problem of nonlinear dynamic data of industrial robots. Modeling and Monitoring Issues

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