Convex relaxation global optimization algorithm-based robot hand-eye calibration method

Abstract

The invention provides a quaternion theory-based convex relaxation global optimization algorithm-based robot hand-eye calibration method which is used for solving a problem that a non-linear optimization-based hand-eye calibration algorithm cannot ensure that an object function converges to a global minimum value when deviations are found in robot kinematics forward solutions and camera external parameter calibration. Based on consideration of effects exerted on calibration equation solution precision by included angles formed between relative movement rotation shafts on a tail end of a mechanical hand, a random sample and consensus algorithm is used for pre-screening the included angles formed between the rotation shafts in calibration data, a quaternion parameterized rotation matrix is used for building a polynomial geometry error object function and constraints, and a linear matrix inequality convex relaxation global optimization algorithm is used for solving a global optimal hand-eye transformation matrix.

Description

technical field [0001] The invention relates to a robot hand-eye calibration method based on a convex-relaxation global optimization algorithm, relates to the technical field of industrial robot vision calibration, and is used in the technical fields of joint arm coordinate measuring machines, robot surgery, and service robots. Background technique [0002] In recent years, with the rapid development of science and technology, robots have gradually entered our social life, such as in intelligent assembly, autonomous navigation, reverse engineering, welding engineering and other fields have been widely used. As one of the key technologies of robot vision, robot hand-eye calibration has always been a research hotspot in the field of machine vision. In the robot hand-eye vision system, it is often necessary to combine the camera with the robot arm to obtain the relative position information of the target object in space relative to the robot actuator, and then precisely control...

AI Technical Summary

Problems solved by technology

In 2011, Zhao proposed a method of calculating the hand-eye transformation matrix using convex optimization theory, which improved the robustness of the calculation results, but the solution process is more complicated

Some of the above studies only focus on the parameterization method or solution algorithm of the hand-eye calibration equation, and some only analyze some factors that affect the solution accuracy of the calibration equation, and comprehensively consider the solution algorithm of the calibration equation and the research on error influencing factors There are still relatively few, but in fact, the screening of calibration data and the solution algorithm of the calibration equation will directly affect the solution accuracy of the hand-eye transformation matrix, so it is necessary to study together

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