Latest Papers

ASME Journal of Mechanisms and Robotics

  • Intuitive Physical Human–Robot Interaction Using an Underactuated Redundant Manipulator With Complete Spatial Rotational Capabilities
    by Audet JM, Gosselin C. on July 21, 2021 at 12:00 am

    AbstractIn this paper, the concept of underactuated redundancy is presented using a novel spatial two-degrees-of-freedom (2-DoF) gravity-balanced rotational manipulator, composed of movable counterweights. The proposed kinematic arrangement makes it possible to intuitively manipulate a payload undergoing 3-DoF spatial rotations by adding a third rotational axis oriented in the direction of gravity. The static equilibrium equations of the 2-DoF architecture are first described in order to provide the required configuration of the counterweights for a statically balanced mechanism. A method for calibrating the mechanism, which establishes the coefficients of the static equilibrium equations, is also presented. In order to both translate and rotate the payload during manipulation, the rotational manipulator is mounted on an existing translational manipulator. Experimental validations of both systems are presented to demonstrate the intuitive and responsive behavior of the manipulators during physical human–robot interactions.

  • Special Section: Mobile Robots and Unmanned Ground Vehicles
    by Reina G, Das TK, Quaglia G, et al. on July 21, 2021 at 12:00 am

    Inspired by the fifth-year anniversary celebration of the homonymous symposium at the International Mechanical Engineering Congress & Exposition (IMECE), this Special Section with ten articles shares the latest research efforts in design, theory, development, and applications for mobile robots and unmanned ground vehicles.

The Series Elastic Gripper Design, Object Detection, and Recognition by Touch

Abstract

In recent years, robotic applications have been improved for better object manipulation and collaboration with human. With this motivation, the detection of objects has been studied with a series elastic parallel gripper by simple touching in case of no visual data available. A series elastic gripper, capable of detecting geometric properties of objects, is designed using only elastic elements and absolute encoders instead of tactile or force/torque sensors. The external force calculation is achieved by employing an estimation algorithm. Different objects are selected for trials for recognition. A deep neural network (DNN) model is trained by synthetic data extracted from standard tessellation language (STL) file of selected objects. For experimental setup, the series elastic parallel gripper is mounted on a Staubli RX160 robot arm and objects are placed in pre-determined locations in the workspace. All objects are successfully recognized using the gripper, force estimation, and the DNN model. The best DNN model is capable of recognizing different objects with the average prediction value ranging from 71% to 98%. Hence, the proposed design of the gripper and the algorithm achieved the recognition of selected objects without the need for additional force/torque or tactile sensors.
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