Gait generation plays a significant role in the quality of locomotion of legged robots. This paper presents the development of multi-phase dynamic equations and optimal trajectory generation for a seven-link planar-biped robot walking on the ground level with consideration of feet rotation in the double support phase. The main contribution of this paper is to increase the stability margin at the phase transition time for simultaneous feet rotation in double support phase by introducing a new style of feet rotation. First, the derivation of the dynamics equations, which is a challenging problem due to the existence of the holonomic constraints, is performed using the Lagrangian formulation. Then, an analytical solution to inverse kinematics is proposed to determine the angles of each joint. A multi-objective genetic algorithm-based optimization technique is proposed to obtain the key parameters in trajectory generation so that the zero moment point tracks a predefined stable trajectory and additionally minimizes the power consumption, which is subjected to actuators’ powers limitations. The effect of the hip height on the total power consumption is also investigated. The numerical simulations demonstrate the effectiveness of the proposed method.

This paper presents a trajectory generation approach for a 7-DOF biped robot on level ground. Simultaneously rotation of feet in double support phase is considered which leads to high-speed and more similar to human being walking. The zero moment point (ZMP) stability criterion is used to ensure the stability of the bipedal walking robot. Since ZMP trajectory in human walking does not stay fixed, it needs to be a straight line shaped forward ZMP trajectory to have a natural walk. A genetic algorithm based method is proposed to obtain key parameters in trajectory generation such that the ZMP follows a predefined trajectory while minimizing power consumption. Simulation results demonstrate the effectiveness of the proposed method.

In recent years, many studies on bipedal walking robots and control algorithms have been conducted. However, different conditions and circumstances that have to be taken into account make the control of biped walking robots a big challenge. This paper proposes the implementation of a new hybrid intelligent control approach for a seven-link biped walking robot to track the specified trajectory based on a compensatory neurofuzzy network and fuzzy controller. This algorithm consists of two main parts: the feedforward compensator includes an integrated compensatory neurofuzzy network for identification of the inverse dynamics model which attempts to cancel the dynamics of the robot, and the feedback controller which includes a Mamdani-type fuzzy controller to compensate the modeling error and the effect of noise on the system. Moreover, a new style of membership functions distribution for fuzzy controller with noise reduction capability is proposed and its influence on the performance of the robot under noise-free and noisy conditions is investigated. Simulations performed on a biped robot illustrate the methods and their performance. The results confirm the high tracking capability and effectiveness of the proposed control approach.

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