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.

Whole-Body Dynamics for Humanoid Robot Fall Protection Trajectory Generation with Wall Support

“…This article uses the arms to reduce the impact when the robot lands, while also balancing and controlling the robot's body. Currently, research on robots falling against walls and placing their arms on walls is mainly report in [18,21]. They used the maximum stiffness ellipse method to determine the pose of the robot arm.…”

“…Inspired by human behavior, Wang Shihao introduced an online control method in his articles [19,20], but the miniature size of his robot meant the actual effects were not fully demonstrated. Da Cui simulated a robot bracing against a wall after being disturbed in his paper [21], modeling the robot's ankle joint as a passive joint. While convenient for simulation, this method would be very dangerous in actual robot applications.…”

First do not fall: learning to exploit a wall with a damaged humanoid robot

“…Inspired from how humans react when falling in front of a wall, Cui et al [16] recently proposed to move the robot arms in a way that maximizes the ellipsoid stiffness, thus increasing stability and shock absorption. Compared to the present work, Cui et al only worked on collisions with a frontal wall and, more importantly, with an intact robot whose model is known.…”