Bipedal robot locomotion modelling with virtual height inverted pendulum and preview control approaches in Simulink environment

Abstract

Control algorithms development for humanoid locomotion is difficult due to humanoid's high number of Degrees of Freedom and system nonlinearity. Moreover, a humanoid may lose balance as its walking speed changes. We present AR-601M humanoid locomotion modelling with virtual height inverted pendulum and preview control approaches. A step length and period influence on walking stability were investigated and simulated within Simulink environment. Achievable in simulation maximal torques in leg joints and corresponding trajectories were verified against attainable robot motors' values.

Swing Leg Trajectory Optimization for a Humanoid Robot Locomotion

“…For the first case hip height was fixed to 0.5 m in order to ensure optimal locomotion speed of the robot based on our previous empirical studies (Khusainov et al, 2016b, Khusainov et al, 2016a. According to joint limits and link parameters we selected the robot step length to be 0.4 m. For these parameters hip and knee joint angles in their starting position were set to 0.1 and 0.55 rad correspondingly; at the end of the trajectory (goal position) hip and knee joint angles were set to -0.66 and 0.55 rad correspondingly.…”

“…ZMP stability constraint equations are used to determine trajectory of Center of Mass (CoM) for the robot (Mitobe et al, 2000). ZMP approach is also used for trajectory generation based on Inverted Pendulum Model (Majima et al, 1999, Khusainov et al, 2016b and Preview Control (Kajita et al, 2003, Park andYoum, 2007). In these methods CoM trajectory is a result of analytical solution of dynamic equations for minimisation of ZMP error in feedback control.…”

“…Yet, while obtaining a swing leg trajectory, the authors do not take into account maximum joint velocity and acceleration limitations, which actually provide critical constraints for a real robot. A selected trajectory could be energy optimal in theory, but if the robot's motors could not supply required by such trajectory torques, the physical robot will fail to perform such trajectory (Khusainov et al, 2016b). In practice, walking speed is one of the most important performance measures for bipedal robots.…”

Stable walking of biped robot based on center of mass trajectory control

“…Given dynamic degree of biped walking, according to the relation between projection of center of mass (CoM), ZMP, and supporting convex polygons, walking can be divided into static walking, quasi-dynamic walking, and dynamic walking. In static walking, the projection of CoM of robot on the ground never exceeds the range of supporting polygons [6][7][8][9]. In dynamic walking, the projection of CoM can exceed the supporting polygon at some point, which requires online control of CoM to generate stable walking pattern [10][11][12][13].…”

Application of Variable Zero-Moment Point in Walking Control of the Biped Robot

“…Owing to the fact that the predictive control follows the idea of human brain's controlling on walking, it has been successfully applied in the advanced robot ASMIO (New ASIMO 2011;Yu et al 2009). As discussed by Kajita et al (2003), the researchers from Japan firstly introduced the predictive control method in walking control of biped robot, and as discussed elsewhere (Khusainov et al 2016;Kunimatsu et al 2008;Park & Youm 2007;Shimmyo et al 2013). Some exploratory investigations have also been carried out by the authors in the present work, and the ranges of parameter values of the predictive controller were studied (Jing et al 2010).…”