Dynamical system based variable admittance control for physical human-robot interaction

Abstract

<jats:sec> <jats:title content-type="abstract-subheading">Purpose</jats:title> <jats:p>The purpose of this paper is to enable robots to intelligently adapt their damping characteristics and motions in a reactive fashion toward human inputs and task requirements during physical human–robot interaction.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title> <jats:p>This paper exploits a combination of the dynamical system and the admittance model to create robot behaviors. The reference trajectories are generated by dynamical systems while the admittance control enables robots to compliantly follow the reference trajectories. To determine how control is divided between the two models, a collaborative arbitration algorithm is presented to change their contributions to the robot motion based on the contact forces. In addition, the authors investigate to model the robot’s impedance characteristics as a function of the task requirements and build a novel artificial damping field (ADF) to represent the virtual damping at arbitrary robot states.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Findings</jats:title> <jats:p>The authors evaluate their methods through experiments on an UR10 robot. The result shows promising performances for the robot to achieve complex tasks in collaboration with human partners.</jats:p> </jats:sec> <jats:sec> <jats:title content-type="abstract-subheading">Originality/value</jats:title> <jats:p>The proposed method extends the dynamical system approach with an admittance control law to allow a robot motion being adjusted in real time. Besides, the authors propose a novel ADF method to model the robot’s impedance characteristics as a function of the task requirements.</jats:p> </jats:sec>

Human-robot collaborative assembly of movable airfoils using adaptive admittance control

“…Since mobility and stability are two important metrics during a motion, the adjustments of the admittance coefficients should take them into account. It has been recognized that virtual damping has a greater effect on human perception than the virtual mass in the admittance control (Bian et al, 2020); however, the performances of the two coefficients are a consistent tendency. Low admittance coefficients (including virtual damping and virtual mass) require less interaction force to accelerate the robot which results in low fatigue, but the precision and the stability in manipulation may reduce due to the reason that the robot is more reactive.…”

“…Since mobility and stability are two important metrics during a motion, the adjustments of the admittance coefficients should take them into account. It has been recognized that virtual damping has a greater effect on human perception than the virtual mass in the admittance control (Bian et al. , 2020); however, the performances of the two coefficients are a consistent tendency.…”

Human-robot force cooperation analysis by deep reinforcement learning

“…Admittance control is widely used to build dynamic relationship in human–robot force cooperation by the mass-spring-damper system (Hogan, 1984). Bian et al (2020) enabled robots to intelligently adapt their damping characteristics and motions in a reactive fashion toward human inputs and task requirements during physical human–robot interaction. The dynamic performance during the interaction is determined by the admittance parameters containing virtual mass, virtual spring and virtual damping.…”