Walking Motion Generation and Neuro-Fuzzy Control with Push Recovery for Humanoid Robot

Abstract

Push recovery is an essential requirement for a humanoid robot with the objective of safely performing tasks within a real dynamic environment. In this environment, the robot is susceptible to external disturbance that in some cases is inevitable, requiring push recovery strategies to avoid possible falls, damage in humans and the environment. In this paper, a novel push recovery approach to counteract disturbance from any direction and any walking phase is developed. It presents a pattern generator with the ability to be modified according to the push recovery strategy. The result is a humanoid robot that can maintain its balance in the presence of strong disturbance taking into account its magnitude and determining the best push recovery strategy. Push recovery experiments with different disturbance directions have been performed using a 20 DOF Darwin-OP robot. The adaptability and low computational cost of the whole scheme allows is incorporation into an embedded system.

Quickly Obtaining Range of Articulated Rotating Speed for Electrically Driven Large-Load-Ratio Six-Legged Robot Based on Maximum Walking Speed Method

“…Hence, the rotation angles of the leg i are solved for the abductor joint, hip joint, and knee joint, when the position matrix 0 P (i) F has been acquired. According to (13) and (14), the mathematical expressions are written as follows. Meanwhile, the rotation angle θ i of abductor joint can be obtained.…”

“…That linear relationship is called the velocity Jacobian matrix. According to (13), (14), and (15), we can conclude that the motion equations of leg i involve the generalized variable q. Equation (34) can be obtained by deriving the generalized variable q. Then…”

“…Zhao et al [12] proposed the obstacle avoidance and motion planning scheme for a hexapod robot. Mendez-Monroy [13] used a pattern generator to maintain the balance of humanoid robot in the presence of strong disturbance. Tian and Gao [14] provided an efficient motion planning method for a six-legged robot to traverse the terrain with medium irregularities.…”