A State-Dependent Damping Method to Reduce Collision Force and Its Variability

Abstract

This paper investigates the effect of biologically inspired angle-dependent damping profile in a robotic joint primarily on the magnitude and the variability of the peak collision force. Joints such as the knee that experience collision forces are known to have an angle-dependent damping profile. In this paper, we have quantified and compared three damping profiles. Our numerical and experimental results show that the proposed hyperbolic angle-dependent damping profile can minimize both the magnitude and the variability of the peak collision force (average magnitude and variability reduction of ≈ 26% and ≈ 47% compared to the peak constant damping profile). Very often, the variability of the force across the collision between the robot and the environment cause uncertainty about the state variables of the robotic joint. We show that by increasing the slope of the proposed hyperbolic angle-dependent damping profile we can also reduce the variability and the magnitude of post-collision peak displacement and peak velocity compared to those of constant damping profile. This was achieved while reducing the root mean square of power consumed by the robotic joint. Index Terms-Actuation and Joint Mechanisms, Compliance and Impedance Control I. INTRODUCTION M INIMISING collision force and its variability is important for many robotic applications such as legged locomotion and collaborative robots (cobots). There are several methods for collision avoidance [1], [2]. However, there are robotic applications where contact is part of the task, such as legged locomotion, object passing, physical examination, etc. In such tasks, predictability depends on both the collision force and its variability. It is believed that for most animals, one of the top priorities is to minimise the peak collision force to reduce the risk of injury and permanent joint damages Manuscript

Embodied Active Tactile Perception

“…For instance, humans modulate finger behaviour and explore patterns better perceive touch-related information for recognition and discrimination [38]. Humans also actively control the leg joint impedance and gait pattern to maintain stability while sensing in unstructured terrain [39].…”