公开(公告)号：US11618519B2

公开(公告)日：2023-04-04

申请号：US16885527

申请日：2020-05-28

Applicant: BEIJING INSTITUTE OF TECHNOLOGY

Inventor： Qiang Huang ,  Zhangguo Yu ,  Xuechao Chen ,  Chencheng Dong ,  Qingqing Li ,  Libo Meng ,  Gao Huang

IPC: B62D57/02 ,  B25J9/16 ,  B25J13/08 ,  G05D15/01

Abstract: The present invention discloses a method of tracking control for a foot force and moment of a biped robot. According to the method, a double-spring damping model is designed, and a force tracking controller is designed by using an LQR optimization method, so as to realize tracking of the foot force and moment of the biped robot. Further, a desired force on a foot and a desired moment on the foot are calculated through a planned ZMP distribution method, thereby eventually achieving better ZMP tracking of the biped robot and adapting to ground of certain unevenness. According to the present invention, the traditional control method of ZMP tracking to realize stable walking of a biped robot and adapting to uneven ground is abandoned; instead, a desired force and moment on a foot enabling stable walking of the robot are directly calculated, and direct control is performed to realize tracking of the force and moment on the foot, so as to carry out stable control in a more essential and easy-to-implement manner, thereby achieving faster control response, stronger capability of adapting to uneven ground, and ideal ZMP tracking effect.

公开(公告)号：US11511431B2

公开(公告)日：2022-11-29

申请号：US16914836

申请日：2020-06-29

Applicant: BEIJING INSTITUTE OF TECHNOLOGY

Inventor： Qiang Huang ,  Xuechao Chen ,  Zhangguo Yu ,  Tong Wu ,  Mingyue Qin ,  Qingqing Li ,  Libo Meng

IPC: B25J9/16 ,  B25J13/08

Abstract: The present invention provides a method for monitoring a balanced state of a humanoid robot, comprising: acquiring state data of the robot falling in different directions and being stable, forming a support vector machine (SVM) training data set and obtaining, by training, an initial SVM classifier; inputting the state data of the robot to the trained SVM classifier, so that the SVM classifier outputs a classification result; taking statistics on a proportion of cycles judged to have an impending fall in the total number of control cycles within a judgment buffer time after the SVM classifier outputs the classification result, and finally determining a monitoring result of the balanced state of the robot according to the proportion and finally extracting state data of misjudged cycles within the buffer time, adding the state data to the current training data set and updating the SVM classifier, eventually enabling the classifier to achieve the effects of matching motion capabilities of the robot and monitoring the balanced state.