Robot walking control method based on foothold compensator

Abstract

The invention relates to a robot walking control method based on a foothold compensator. The robot walking control method comprises the following steps of firstly, establishing a constraint dynamic model of a robot; secondly, designing the foothold compensator based on the constraint dynamic model according to the constraint dynamic model; thirdly, establishing a heteroscedastic sparse Gauss process regression model and realizing the mapping calculation from input to output of the foothold compensator; fourthly, locally updating the heteroscedastic sparse Gauss process regression model; fifthly, establishing the foothold compensator based on the heteroscedastic sparse Gauss process regression model; sixthly, performing prediction control over the walking of the robot according to the foothold compensator based on the heteroscedastic sparse Gauss process regression model. Compared with the prior art, the robot walking control method disclosed by the invention has the advantages of accurate prediction, high learning speed and the like.

Description

technical field [0001] The invention relates to the field of robot motion control, in particular to a robot walking control method based on a foothold compensator. Background technique [0002] Dynamically adjusting the foothold of the humanoid robot can adjust the dynamic characteristics and controllability of the robot, which in turn can improve the robot's ability to suppress unknown disturbances. At present, the realization of foothold compensator is mostly based on simple linear controller, but for humanoid robot, its walking process is a complex nonlinear system. Therefore, even if the linear foothold compensator can obtain better foothold compensator gain parameters through the learning method, there is still a non-negligible error between the final foothold compensator strategy and the dynamic characteristics of the robot. In addition, if the foothold compensator learning process of the humanoid robot takes a long time, it is not suitable for the robot to learn fast...

AI Technical Summary

Problems solved by technology

At present, the realization of the foothold compensator is mostly based on a simple linear controller, but for a humanoid robot, its walking process is a complex nonlinear system

Therefore, even if the linear foothold compensator can obtain better foothold compensator gain parameters through the learning method, there is still a non-negligible error between the final foothold compensator strategy and the dynamic characteristics of the robot.

In addition, if the foothold compensator of the humanoid robot takes a long time to learn, it is not suitable for the robot to learn fast and real-time foothold compensation skills in an unknown environment.

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