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Recently, there has been an increase in research focussed towards fall prevention strategies.
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These mentioned strategies mainly focus on how to reduce damage during contact. To generate on-purpose impacts with rigid objects, Wang et al proposed an impact-aware control method with task-space quadratic optimisation. To minimise damage, falling trajectory optimisation, pose reshaping and adaptive compliance control have been proposed when a fall is inevitable.
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However, falling remains a risk when excess external forces or obstacle constraints are imposed. These can be categorised into: stepping, ankle, ankle–hip, and online learning strategies. Similar to humans, humanoid robots are also at risk of falling, even though various balance control methods have been proposed. In, Borrelli et al concluded that protective arm reaction strategies are modulated according to fall height. Lattimer et al investigated age-related differences in upper limb strength and fall arrest strategy differences during a simulated fall and assessed the relationships between muscle strength and biomechanical variables. Kim and Ashton-Miller isolated critical biomechanical factors in fall arrests, such as contact pose and joint velocity during contact, by simulating a bimanual forward fall. Chou et al investigated the effect of elbow flexion on the upper limb impact forces during a fall. DeGoede and Ashton-Miller measured the effects of a fall arrest strategy on the peak hand impact force and explored the strength requirement necessary to arrest a fall. Chiu and Robinovitch proposed a model to predict impact forces during falls on the outstretched hand. Therefore, identifying potential factors that lead to successful and safe fall arrest strategies has gained much attention.
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However, the protective arm movements are not without risk, as they often become a site of fall-related injuries. A common strategy to arrest the falls of humans is to use their arms to protect the head, trunk and hip. The results show that using the proposed strategy can reduce the joint torque during impact when the arms are used to arrest the fall.ĭue to the inherent instability of bipedal locomotion, falls are inevitable for humans, despite their possession of excellent balance. 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. During contact, the upper limb acts as an adjustable active spring–damper and absorbs impact shock to steady itself. 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. We propose a hypothesis that humans naturally favour to select a pose that can generate a suitable Cartesian stiffness of the arm end-effector. Once the fall is inevitable, the arm of the robot will be actuated to gain contact with an environmental object to prevent falling.
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Firstly, the capture point method is used to detect falling. Inspired by human fall arrest, we present a novel humanoid robot fall prevention strategy by using arms to make contact with environmental objects. Falls are a common risk and impose severe threats to both humans and humanoid robots as a product of bipedal locomotion.