Abstract
Humanoid robots are versatile robot platforms that can carry out intelligent tasks and services for humans, including intimate interactions. For high mobility, a robust stabilization of motion including biped walking is crucial. This paper employs and elaborates on sensory reflex control to stabilize standing motion and biped walking using basic sensors such as an inertial measurement unit (IMU) and a force-sensing resistor (FSR). Specifically, normalized zero-moment points processed from FSR data are used in the reflexive control of a simple motion of swinging the whole body while standing, and the measured inclination angle of the trunk, filtered from IMU data, is used for biped walking on a sloped floor. The proposed control scheme is validated through experiments with the commercial humanoid robot, ROBOTIS-OP.
Highlights
Humanoid robots are designed and manufactured to resemble the human body, which is advantageous for providing effective human and robot interaction (HRI), intelligent services and displaying emotions through expression
Since the humanoid robot stands on one leg or on both, the normalized ZMP (NZMP) needs to be averaged in case of a double support phase
The NZMP trajectory attained from the result of ZMPbased SRC (ZSRC) is detached from the upper bound and shortly stays at the lower bound instead
Summary
Humanoid robots are designed and manufactured to resemble the human body, which is advantageous for providing effective human and robot interaction (HRI), intelligent services and displaying emotions through expression. Motion stabilization control employs an inverted pendu‐ lum control [4], a central pattern generator [5], neural networks [6] and a fuzzy control [7] for the real-time adjustment of joint angles during biped walking, using all the sensory information available. Two types of SRCs—one based on ZMP and the other on trunk inclination angles—are proposed with novel control schemes to stabilize humanoid motion while standing and during biped walking. As for the biped walking, reference joint patterns are generated offline for the robot to walk on level ground using an optimization method, which traces the referent ZMP trajectory and swing foot trajectory [10].
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