6533b870fe1ef96bd12d06ca

RESEARCH PRODUCT

An Input Observer-Based Stiffness Estimation Approach for Flexible Robot Joints

Maja TrumicKosta JovanovicAdriano Fagiolini

subject

0209 industrial biotechnologyControl and OptimizationFlexibility (anatomy)Observer (quantum physics)Computer scienceBiomedical Engineering02 engineering and technologyCalibration and identificationComputer Science::Robotics020901 industrial engineering & automationArtificial IntelligenceControl theorymedicineTorqueFlexible RobotMechanical Engineeringnatural machine motionStiffness021001 nanoscience & nanotechnologyComputer Science ApplicationsHuman-Computer Interactionmedicine.anatomical_structureControl and Systems EngineeringJoint stiffnessRobotComputer Vision and Pattern Recognitionmedicine.symptomDeformation (engineering)0210 nano-technologyActuatorfailure detection and recovery

description

This letter addresses the stiffness estimation problem for flexible robot joints, driven by variable stiffness actuators in antagonistic setups. Due to the difficulties of achieving consistent production of these actuators and the time-varying nature of their internal flexible elements, which are subject to plastic deformation over time, it is currently a challenge to precisely determine the total flexibility torque applied to a robot's joint and the corresponding joint stiffness. Herein, by considering the flexibility torque acting on each motor as an unknown signal and building upon Unknown Input Observer theory, a solution for electrically-driven actuators is proposed, which consists of a linear estimator requiring only knowledge about the positions of the joints and the motors as well as the drive's dynamic parameters. Beyond its linearity advantage, another appealing feature of the solution is the lack of need for torque and velocity sensors. The presented approach is first verified via simulations and then successfully tested on an experimental setup, comprising bidirectional antagonistic variable stiffness actuators.

https://doi.org/10.1109/lra.2020.2969952