Tensor-product-model-transformation-based mobile robot control method

Abstract

The invention discloses a tensor-product-model-transformation-based mobile robot control method. The mobile robot is composed of a web camera, a PC machine, a vision sensor, a prediction controller and a dual-wheel drive robot body. A tensor product (TP) model transformation principle is introduced into image vision servo control and thus jacobian vertex matrix of an image is obtained; and a restriction problem of the mobile robot is classified into solution of a convex optimization problem by linear matrix inequality, thereby obtaining a control signal, so that the system has closed-loop asymptotic stability. According to the invention, the mobile robot can be driven from a randomly designated pose to an expected vision feature location pose. While the system input restraint is guaranteed, the feature point does not exceed the view field. Compared with traditional IBVS, the method is characterized in that the inverse of the image jacobian matrix is solved directly, thereby avoiding consideration of image singularity. Compared with prediction controlling, the good real-time performance is realized. The system has closed-loop asymptotic stability.

Description

technical field [0001] The invention relates to a mobile robot control method based on tensor product model transformation, belonging to the field of mobile robot control. Background technique [0002] Observing the characteristics of image-based mobile robot visual servo control methods at home and abroad in recent years, it mainly focuses on the online estimation of the image Jacobian matrix. Zeng Xiangjin, Huang Xinhan and others used the Broyden dynamic method to estimate the Jacobian matrix online to control the robot's motion. The method is self-adaptive, but the stability of the visual servo control algorithm cannot be guaranteed when the motion parameters of the robot cannot be identified or large deviations appear in the image. Gao Zhendong and others used the method of local fitting to estimate the image Jacobian matrix, which can obtain a more accurate value of the image Jacobian matrix, and can compensate for the time delay caused by image processing. However, ...

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Problems solved by technology

However, none of these research works considered intrinsic constraints, including drive saturation, maximum velocity, and constraints on some state variables of robot dynamics

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