This paper presents a novel framework of hydraulic-driven biped robot. The target biped robot is a humanoid robot NWPUBR-1 with 12 degrees of freedom (DOF), and its dimensions are close to that of an average male. The joint axis adopts the modular structure design of sensor with angle measurement, and the force sensor is deployed on the side of hydraulic actuator to facilitate force and position control of the robot. Meanwhile, the finite element analysis of critical components is conducted to meet the requirements of mechanical strength in motion. Based on the screw theory, the forward kinematics and Jacobian matrix models are established, and the inverse kinematics of robot is solved by using the analytical geometric method. To achieve real-time control of the robot gait in 3D space, a three-dimensional linear inverted pendulum model (3D-LIPM) is built, and a 3D gait model is generated by combining the zero-moment point (ZMP) theory. In the virtual environment of MATLAB software, the results of programming simulation show that the biped robot can walk stably in the virtual environment, which proves the correctness of 3D-LIPM.

This paper presents a simplified method of dynamic equations in a generalized coordinate system, which decouples the relative motion of the front and back links of the robot joint, and maps generalized angular variables to the same angular datum. Because of the decoupling between absolute angle variables, the complexity of the coefficient matrix of the dynamic equation of the system is reduced, which facilitates the application of the actual series robot system. The simplified process is derived in detail, and the equivalent relation between the generalized relative coordinates and the generalized absolute coordinate dynamics is demonstrated. The relation can be extended to the dynamic equations of different generalized coordinate variables. Aimed at the problem that the quasi-input moment vectors obtained in generalized absolute coordinates cannot be directly used without considering the dynamic coupling of the whole system, the linear relationship between the input moment vectors in absolute and relative coordinates is found. In the end, a simplified example of the Euler-Lagrange dynamic equation for multi-degree-of-freedom manipulator is given. The relationship between the two input torque vectors is simulated by MATLAB and verifies that the input torque can be indirectly calculated by finding the quasi input torque under the absolute coordinates.

The gait generation algorithm considering both step distance adjustment and step duration adjustment could improve the anti-disturbance ability of the humanoid robot, which is very important to the dynamic balance, but the step duration adjustment often brings non-convex optimization problems. In order to avoid this situation and improve the robustness of the gait generator, a gait generation mechanism based on flexible model predictive control is proposed in this article. Specifically, the step distance adjustment and step duration adjustment are set to be optimization objectives, while the change of pressure center is treated as the optimal input to minimize those objectives. With the current system state being used for online re-optimization, a feedback gait generator is formed to realize the strong stability of variable speed and variable step distance walking of the robot. The main contributions of this work are twofold. First, a gait generation mechanism based on flexible model predictive control is proposed, which avoids the problem of nonlinear optimization. Second, a variety of feasible optimization constraints were considered, they can be used on platforms with different computing resources. Simulations are conducted to verify the effectiveness of the proposed mechanism. Results show that as compared with those considering step adjustment only, the proposed method largely improves the compensation ability of disturbance and shortens the adjustment time.

As the first step for biped robots to enter the human life, robust walking is a difficult problem to be solved owing to the various algorithms and practical engineering issues being involved. This paper studies the robust walking problem for humanoid robots by using the divergence component of motion (DCM) method based on linear inverted pendulum model. Firstly, we implement a DCM trajectory planning method to simplify the planning process. It calculates the DCM trajectories under the requirements of walking speed and initial state of the system. Then, a DCM feedback controller with anti-disturbance ability is proposed to realize the tracking control of the planned trajectory. Finally, the optimization method and DCM feedback control are integrated into a hybrid optimization controller, which takes into account the step adjustment and the step duration adjustment of the robot. Simulation results demonstrates that the technique can act naturally stable inside an enormous scope of effect unsettling influences, the maximum recoverable impact of a humanoid robots with a mass of 70Kg can reach 85Ns, which is much better than the 20Ns of the existing model-based prediction control method.