Identification of macroscopic feedback gain in a position-controlled humanoid robot and its application to falling detection

Motion control of a humanoid robot is challenging problem because its dynamics is complicated. To make it easier to design a controller, a macroscopic dynamics focusing on the relationship between the center of gravity (COG) and the center of pressure (COP) is often used. Based on the COG-COP model, it is possible to control the COG by using the COP as the control input. Moreover, it is possible to detect falling based on the Maximal Output Admissible (MOA) set as the author presented in the previous paper. However, generating appropriate control input (COP) requires sophisticated joint actuator which can realize torque control and backdrivability. Research on this type of actuators is still on-going in the robotics field, and most existing humanoid robots are position-controlled. In this paper, the author identifies a macroscopic feedback gain in a position-controlled humanoid by measuring a disturbance response. From the obtained feedback gain, it is possible to compute the MOA set and apply it to the falling prevention control. The effectiveness of the proposed method is verified with experiments.