Latest Papers

ASME Journal of Mechanisms and Robotics

  • Design of Reconfigurable Articulated Walking Mechanisms for Diverse Motion Behaviors
    on March 20, 2025 at 12:00 am

    AbstractLegged robots are able to move across irregular terrains and those based on 1-degree-of-freedom planar linkages can be energy efficient but are often constrained by a limited range of gaits which can limit their locomotion capabilities considerably. This article reports the design of novel reconfigurable parallel linkages that not only produce different walking patterns but also realize behaviors beyond locomotion. Experiments with an implemented wearable device able to guide the lower extremity through multiple human-like walking trajectories are presented and the preliminary results validate the proposed approach.

  • Modeling, Kinematics, and Dynamics of a Rigid-Flexible Coupling Spring-Cable-Driven Parallel Robot
    on March 20, 2025 at 12:00 am

    AbstractConventional parallel robots are made of rigid materials for the purpose of fast and accurate localization, exhibiting limited performance in large-scale operations. Inspired by the softness and natural compliance of biological systems, this article proposes a rigid-flexible coupling cable-driven parallel robot. The concept of flexible cable and spring hybrid and working principle are introduced. The kinematics of single module and multiple modules connected in series are analyzed and equations are given, and the Lagrange equation is used to establish dynamic models. Finally, two methods are used to validate the kinematics and dynamics. One is to draw the specific structure with the posture of the end-effector and measure the cable length to compare it with the analytical solution in the kinematic model. The other is to build the structure and joint characteristics in simulink, given the posture of the end-effector and the external force/torque, the cable length and the force applied are compared with those obtained from the dynamic model. The reasonableness of the mechanism and the feasibility of the kinematic and dynamic models are verified.

Learning-Based Position and Orientation Control of a Hybrid Rigid-Soft Arm Manipulator

Abstract

We present a position and orientation controller for a hybrid rigid-soft manipulator arm where the soft arm is extruded from a two degrees-of-freedom rigid link. Our approach involves learning the dynamics of the hybrid arm operating at 4Hz and leveraging it to generate optimal trajectories that serve as expert data to learn a control policy. We performed an extensive evaluation of the policy on a physical hybrid arm capable of jointly controlling rigid and soft actuation. We show that with a single policy, the arm is capable of reaching arbitrary poses in the workspace with 3.73cm (<6% overall arm length) and 17.78 deg error within 12.5s, operating at different control frequencies, and controlling the end effector with different loads. Our results showcase significant improvements in control speed while effectively controlling both the position and orientation of the end effector compared to previous quasistatic controllers for hybrid arms.

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