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Modular Magnetorheological Actuator With High Torque Density and Transparency for the Collaborative Robot Industry

Catherine Veronneau, Jeff Denis, Pierre Lhommeau, Alexandre St-Jean, Alexandre Girard, Jean‐Sébastien Plante, Jean-Philippe Lucking Bigué

2022IEEE Robotics and Automation Letters23 citationsDOI

Abstract

In the last decade, the development of simple and easy-to-use robotics has provided a significant step towards the democratization of collaborative robots, which can now work closely alongside humans. However, state-of-the-art cobots still provide safety at the cost of work-speed, which is purposely limited to avoid high-energy collisions. In order to increase safety and work speed simultaneously, force/impedance-controlled actuators with high dynamic performance, backdrivability and low-reflected inertia are of interest. In this letter, a novel robotic actuator architecture with two independent magnetorheological (MR) power chains is proposed, built and experimentally characterized. The modular 30 Nm robotic joint is shown to have similar size (85 X 85 X 124 mm), weight (1.1 kg) and torque density (27 Nm/kg) as the Universal Robot (UR) size 1 robotic joint, which relies on a Harmonic Drive (HD) gearbox, but with 150 times less inertia, high backdrivability (0.7% of max torque), high torque bandwidth (>70 Hz) and inherent torque-limiting that makes it immune to impacts. The dual MR power chain allows ultra-fast transparent interactions with bluezero backlash over half its torque range and peak torque when motor reversal is allowed. To illustrate the force/impedance control performance, a 3-DOF robotic arm is built and used to simulate virtual objects in both open and closed loop.

Topics & Concepts

TorqueRotary actuatorActuatorRoboticsRobotComputer scienceHarmonic driveWork (physics)Control theory (sociology)EngineeringSimulationMechanical engineeringArtificial intelligencePhysicsControl (management)ThermodynamicsProsthetics and Rehabilitation RoboticsSoft Robotics and ApplicationsRobot Manipulation and Learning
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