A Servo Tracking Accelerated Convergence Method for Robot Vision Feature Planning Trajectory

A robot vision and visual feature technology, applied in manipulators, program-controlled manipulators, manufacturing tools, etc., can solve problems such as control interruption and loss of important visual information, reduce the number of steps, avoid excessive motion errors, and ensure global convergence. Effect
CN111283683BActive Publication Date: 2022-02-01HUNAN NORMAL UNIVERSITY
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HUNAN NORMAL UNIVERSITY
Publication Date
2022-02-01

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Abstract

The invention discloses a servo tracking acceleration convergence method for a robot visual feature planning trajectory, comprising the following steps: S101, establishing a visual feature trajectory planning model, and obtaining a specific planning trajectory; S102, evenly dividing parameter variables, and performing segmented visual servo tracking; S103, define the maximum displacement error and the maximum rotation error generated by tracking; S104, under the constraint of the maximum displacement error, minimize the total tracking convergence time. The present invention defines the tracking error, sets the trajectory interpolation, and performs multi-parameter optimization to achieve tight tracking and obtain global convergence, while reducing the tracking convergence time as much as possible, and improving the self-adaptive ability of the visual servo system in the face of dynamic targets and environments.
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Description

technical field

[0001] The invention relates to a servo tracking acceleration convergence method for a robot visual feature planning trajectory, belonging to the field of robot sensing and servo control. Background technique

[0002] The robot visual servo control system uses visual perception information to establish the conversion relationship between the visual feature error and the movement rate of the robot's end camera, which is used to guide the movement of the robot and gradually reduce the visual feature error. the expected pose of .

[0003] When the amount of rotation from the desired pose is large, the composition of the eigenvectors used as control inputs and their corresponding transformation matrices are likely to cause local convergence of the features, that is, when the eigenvalues ​​converge, the camera pose has not yet converged. When the displacement is large, it may cause the loss of important visual information during the servo process, resulting in th...

Claims

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