Multi-cell-ellipsoid dual-filtering-based pose positioning method for mobile robot in production workshop

Abstract

The invention discloses a multi-cell-ellipsoid dual-filtering-based pose positioning method for a mobile robot in a production workshop. The method comprises the following steps: firstly, establishinga mobile robot pose positioning linearized model in a production workshop, and obtaining an input and output sequence of the mobile robot pose positioning linearized model according to a system model; performing dual identification on system parameters by using a full-symmetric polytope method and an ellipsoid method in set membership identification, firstly, performing first identification by using an ellipsoid, and meanwhile, obtaining a full-symmetric polytope with the minimum volume at the moment according to a full-symmetric polytope iterative formula; then updating the holosymmetric polytope to enable the holosymmetric polytope to contain the changed parameters; carrying out singular value decomposition dimensionality reduction on the updated full-symmetric polytope to obtain a full-symmetric polytope containing a parameter feasible set after dimensionality reduction, and finally, dispersing the full-symmetric polytope after dimensionality reduction into a constraint condition to update an ellipsoid for the second time to obtain a system parameter value. Through singular value decomposition, the dimensionality of the full-symmetric polytope is not increased all the time andis always maintained at the initially given dimensionality, so that the calculation complexity is reduced, and the identification precision is improved while the identification conservative property is reduced through twice ellipsoid updating.

Description

technical field [0001] The invention relates to a polycellular-ellipsoid filter technology for pose positioning of a mobile robot in a production workshop, and belongs to the technical field of advanced manufacturing process control. Background technique [0002] With the progress of society and the development of science and technology, automation technology has gradually replaced labor and is widely used in intelligent manufacturing, agricultural production and national defense industry. Robot technology with high integration of automation technology has also been applied in production and life. In the era of smart factories, the application of robots gradually replaces manual labor, greatly increasing production capacity and reducing the rate of defective products. [0003] Compared with high-precision robots in the medical field, due to the manufacturing process and cost requirements, the self-positioning of mobile robots currently used in production workshops has the p...

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Problems solved by technology

[0003] Compared with high-precision robots in the medical field, due to the production process and cost requirements, the self-positioning of mobile robots currently used in production workshops has the problem of low positioning accuracy

In the research on the positioning of mobile robots for workshops, Zhou B, Qian K, Ma X, et al. Ellipsoidal bounding set-membership identification approach for robust fault diagnosis with application to mobile robots[J]. Systems Engineering and Electronic Technology (English version) ,2017(5).B. and Zhou,X.Z.Dai.Robust fault detection of mobile robots using an ellipsoid bounding identification algorithm.Proc.Of the IEEE International Conference on Mechatronics and Automation,2011:2195-2200.Using ellipse method to locate production workshop movement The pose of the robot, the error of this type of method will increase with the increase of the number of iterations in the process of solving the distance from the center of the ellipse to the constraint plane and then normalizing, resulting in low positioning accuracy, and continuous iterations lead to the failure of this type of method There is a problem of large amount of calculation

Alamo T, Bravo J M, Camacho E F.Guaranteed state estimation by zonotopes[C] / / IEEEConference on Decision& Control.IEEE,2005. It is given that the multi-cell filtering method is used to realize the positioning of the robot pose, but this method is due to the In the iterative process, the fully symmetrical polyhedral body with the fewest sides is selected, so the upper and lower boundaries of the predicted value of each step of the robot pose are large, and the prediction is highly conservative, so the positioning accuracy is not high.

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