Knee-stretched walking with toe-off and heel-strike for a position-controlled humanoid robot based on model predictive control

Knee-stretched walking is considered to be a human-like and energy-efficient gait. The strategy of extending legs to obtain vertical center of mass trajectory is commonly used to avoid the problem of singularities in knee-stretched gait generation. However, knee-stretched gait generation utilizing this strategy with toe-off and heel-strike has kinematics conflicts at transition moments between single support and double support phases. In this article, a knee-stretched walking generation with toe-off and heel-strike for the position-controlled humanoid robot has been proposed. The position constraints of center of mass have been considered in the gait generation to avoid the kinematics conflicts based on model predictive control. The method has been verified in simulation and validated in experiment.

Active Gaits Generation of Quadruped Robot Using Pulse-Type Hardware Neuron Models

In this chapter, the authors will propose the active gait generation of a quadruped robot. We developed the quadruped robot system using self-inhibited pulse-type hardware neuron models (P-HNMs) as a solution to elucidate the gait generation method. We feedbacked pressures at the robot system’s each foot to P-HNM and varied the joints’ angular velocity individually. We experimented with making the robot walk from an upright position on a flat floor. As a result of the experiment, we confirmed that the robot system spontaneously generates walk gait and trot gait according to the moving speed. Also, we clarified the process by which the robot actively generates gaits from the upright state. These results suggest that animals may generate gait using a similarly simple method because P-HNM mimics biological neurons’ function. Furthermore, it shows that our robot system can generate gaits adaptively and quite easily.

Applying Improve Differential Evolution Algorithm for Solving Gait Generation Problem of Humanoid Robots

This chapter addresses an approach to generate 3D gait for humanoid robots. The proposed method considers gait generation matter as optimization problem with constraints. Firstly, trigonometric function is used to produce trial gait data for conducting simulation. By collecting the result, we build an approximation model to predict final status of the robot in locomotion, and construct optimization problem with constraints. In next step, we apply an improve differential evolution algorithm with Gauss distribution for solving optimization problem and achieve better gait data for the robot. This approach is validated using Kondo robot in a simulated dynamic environment. The 3D gait of the robot is compared to human in walk.