Human inspired fall arrest strategy for humanoid robots based on stiffness ellipsoid optimisation

Abstract

Falls are a common risk and impose severe threats to both humans and humanoid robots as a product of bipedal locomotion. Inspired by human fall arrest, we present a novel humanoid robot fall prevention strategy by using arms to make contact with environmental objects. Firstly, the capture point method is used to detect falling. Once the fall is inevitable, the arm of the robot will be actuated to gain contact with an environmental object to prevent falling. We propose a hypothesis that humans naturally favour to select a pose that can generate a suitable Cartesian stiffness of the arm end-effector. Based on this principle, a configuration optimiser is designed to choose a pose of the arm that maximises the value of the stiffness ellipsoid of the endpoint along the impact force direction. During contact, the upper limb acts as an adjustable active spring-damper and absorbs impact shock to steady itself. To validate the proposed strategy, several simulations are performed in MATLAB & Simulink by having the humanoid robot confront a wall as a case study in which the strategy is proved to be effective and feasible. The results show that using the proposed strategy can reduce the joint torque during impact when the arms are used to arrest the fall.