A robot"hand-eye"pose autonomous coordination was regarded as a uncalibration con-strained programming problem,and a visual closed-loop control method was proposed based on sec-ond-order cone constrained programming.Firstly,the visual servoing control algorithms were con-structed in the image planes and Cartesian space,respectively based on images and positions.After that,by established the path constraint and the local minimal constraint rules,and a second-order cone convex optimization model was constructed to realize the compromise optimal control of image feature trajectory and robot motion path.Moreover,the proposed second-order cone constrained pro-gramming model was embedded with an adaptive state estimator,to realize robotic Jacobian matrix online mapping learning,and to solve the unknown problems of"hand-eye"calibration parameters and visual depth information.Finally,the uncalibrated robot visual positioning experiments prove the ef-fectiveness of the convex optimization planning model,and the real grasping tasks illustrate the feasi-bility of the robot pose autonomous coordination.
pose coordinationconstrained programminguncalibration visual servoinghybrid closed-loop feedback control
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