Intelligent hierarchical compensation method for industrial robot positioning error based on compound branch neural network automatic creation

Absolute positioning accuracy is a crucial index for evaluating industrial robot performance and the foundation for motion trajectory and machining accuracy. Current positioning error compensation methods focus on achieving unified compensation within a robot’s workspace. These methods rely heavily on expert knowledge and require a significant amount of manual intervention. To realize refined error compensation and improve the autonomy and intelligence degree of a robot, an intelligent hierarchical positioning error compensation method based on a master–slave controller is proposed in this paper. Specifically, positioning error compensation is addressed through two research questions related to positioning error level diagnosis and compensated pose prediction, and the approach consists of two major processes: automatic creation of a compound branch compensation network and hierarchical positioning error compensation. For the first process, the master controller independently grades the positioning error levels and directs the diagnosis slave controller to create a positioning error level diagnosis model in terms of the robot pose error data. Then, it directs the prediction slave controller to create several compensated pose prediction models based on the pose data of different error levels. Subsequently, the diagnosis and prediction models are integrated to form a compound branch compensation network. For the second process, the master controller first activates the diagnosis branch of the compound branch compensation network to determine the positioning error level of the current robot pose. Then, it activates the prediction branch corresponding to the determined error level to generate the compensated pose. Finally, it uses the diagnosed error level to filter the compensated pose. Experimental cases of a Stäubli robot and a UR robot are applied to verify the feasibility and effectiveness of the proposed method. The experimental results show that the proposed method reduces the positioning error of the Stäubli robot from 0.848 to 0.135 mm and the UR robot from 2.11 to 0.158 mm, outperforming relevant current methods.