Robot force and position hybrid control method and device based on Gaussian process regression

By combining Gaussian process regression and convex optimization methods, adaptive variable impedance control of robot force-level hybrid control is realized, solving the accuracy and flexibility of force-level hybrid control in complex environments, and improving the control performance of robots in human massage and physiotherapy.

CN119734275AActive Publication Date: 2025-04-01ZHEJIANG UNIV OF TECH
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Patent Information

Application Number
CN202510077983.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-01-17
Publication Date
2025-04-01
Estimated Expiration
2045-01-17

AI Technical Summary

Technical Problem

The prior art is difficult to achieve high precision and flexibility of robotic power level hybrid control in complex and dynamic environments, especially in human massage and physiotherapy scenarios, which are difficult to balance the dynamic performance and steady-state errors of force tracking.

Method used

The robot's power-level hybrid control method based on Gaussian process regression is adopted, and the robot's end adaptive variable impedance control is realized through the combination of Gaussian process regression and convex optimization. This method collects position and contact force data in real time, constructs a Gaussian process regression model, optimizes the stiffness coefficient, and dynamically adjusts the impedance parameters to achieve constant contact force control.

Benefits of technology

The accuracy of the robot's terminal contact force control is improved, and it can maintain constant force application in complex dynamic environments, and dynamic adjustments are made according to environmental feedback, which enhances the robot's control performance in human massage and physiotherapy.

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Abstract

The invention belongs to the technical field of robot motion control, and discloses a robot force-position hybrid control method and device based on Gaussian process regression, and the method comprises the steps: constructing a Gaussian process regression model of a stiffness coefficient; inputting the speed obtained in real time, the position error of the robot tail end and the contact force error between the robot tail end and the environment into the trained Gaussian process regression model; taking a rigidity coefficient output by the Gaussian process regression model as an optimization reference value, constructing a convex optimization problem, and solving the convex optimization problem to obtain an optimized rigidity coefficient; and in combination with an impedance control model of the robot, according to the optimized rigidity coefficient, the expected position of the tail end of the robot and the expected contact force between the tail end of the robot and the environment are obtained, and constant contact force control over the tail end of the robot in the operation process is achieved. Impedance parameters are dynamically adjusted according to environment feedback, and the precision of robot tail end contact force control is improved.
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