Humanoid Robot Locomotion Control based on Perceptive Model A humanoid locomotion control method with humanlike perceptive model is described in this paper. Traditionally the locomotion problem of biped robot is divided into temporal periodic gait generation and realtime stabilizing control. However due to unmeasurable accumulated errors from simplified dynamic model and other uncertain disturbances, the spatial and temporal deviation from the motion plans askes for a new perceptive locomotion planning and control system which is decoupled with time, rather than adhering to a rigid time schedule regardless of current dynamic balance state. This method also gives the right of interpretation of nontime motion plan of gait to the motion control layer based on a designed perceptive reference, which is directly ensure dynamic balancing and high response rate to the errors. The simulation and experiment on a fullsize humanoid robot show the success of the method.

A Robust Biped Locomotion Based on LinearQuadraticGaussian Controller and Divergent Component of Motion Generating robust locomotion for a humanoid robot in the presence of disturbances is difficult because of its high number of degrees of freedom and its unstable nature. In this paper, we used the concept of Divergent Component of Motion (DCM) and propose an optimal closedloop controller based on LinearQuadraticGaussian to generate a robust and stable walking for humanoid robots. The biped robot dynamics has been approximated using the Linear Inverted Pendulum Model (LIPM). Moreover, we propose a controller to adjust the landing location of the swing leg to increase the withstanding level of the robot against a severe external push. The performance and also the robustness of the proposed controller is analyzed and verified by performing a set of simulations using MATLAB. The simulation results showed that the proposed controller is capable of providing a robust walking even in the presence of disturbances and in challenging situations.