Latest Papers

ASME Journal of Mechanisms and Robotics

  • Integrated Wheel–Foot–Arm Design of a Mobile Platform With Linkage Mechanisms
    on March 20, 2024 at 12:00 am

    AbstractInspired by lizards, a novel mobile platform with revolving linkage legs is proposed. The platform consists of four six-bar bipedal modules, and it is designed for heavy transportation on unstructured terrain. The platform possesses smooth-wheeled locomotion and obstacle-adaptive legged locomotion to enhance maneuverability. The kinematics of the six-bar bipedal modules is analyzed using the vector loop method, subsequently ascertaining the drive scheme. The foot trajectory compensation curve is generated using the fixed-axis rotation contour algorithm, which effectively reduces the centroid fluctuation and enables seamless switching between wheels and legs. When encountering obstacles, the revolving linkage legs act as climbing arms, facilitating seamless integration of wheel, foot, and arm. A physical prototype is developed to test the platform on three typical terrains: flat terrain, slope, and vertical obstacle. The experimental results demonstrated the feasibility of the platform structure. The platform can climb obstacles higher than its own height without adding extra actuation.

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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