Latest Papers

ASME Journal of Mechanisms and Robotics

  • Fully Foldable Mechanical Metamaterials With Isotropic Auxeticity and Its Generated Multi-Mode Folding Form
    on February 10, 2025 at 12:00 am

    AbstractAuxetic materials, a type of mechanical metamaterial with negative Poisson's ratio, are potentially utilized in the realms of energy absorption and engineering structures. However, most of the existing auxetic materials either contain a large amount of rotational motion or still have gaps when fully folded, which is not conducive to lifting loads. Besides, their application is limited to flexible environments due to their single-folding mode. To overcome such limitations, a fully foldable mechanical metamaterial with isotropic auxeticity is proposed by utilizing the Sarrus mechanism, and a derivative multi-mode folding form is obtained in this paper. Then, the degrees-of-freedom (DOF), bistability, and kinematic characterizations are analyzed to show the performance of the proposed structures. Finally, the parameters of the proposed fully foldable mechanical metamaterials are discussed to simplify the structures. Some prototypes are fabricated to validate the effectiveness and performance of the proposed mechanical metamaterials. The proposed mechanical metamaterials have some merits, such as isotropic auxeticity, being fully folded to achieve dense compression, being bistable with load-bearing capacity, multi-mode folding form, and single-DOF, and they have versatile potential applications in complex environments requiring large deformation and flexible adaptation.

  • Elastostatic Performance Evaluation of a Full-Mobility Parallel-Kinematics Machine With Flexible Links
    on February 10, 2025 at 12:00 am

    AbstractThe subject of this article is the elastostatics of a novel three-limb, full-mobility parallel-kinematics machine (PKM) with flexible links, intended for high-frequency, small-amplitude operations. The objective is to establish the Cartesian stiffness model and performance indices capable of guiding the structural design of the machines of interest. We base our analysis on what we term an elastostatic Cartesian model: the light-weight limb rods are modeled as identical, massless, linearly elastic beams; the motor shafts and couplings are modeled likewise, with the beams replaced by identical, massless, linearly elastic torsional springs, both link flexibility and actuator flexibility thus being considered. The moving platform is assumed to be the only moving rigid body of the machine. This platform is thus regarded as a rigid body elastically mounted onto the base platform via a six-degree-of-freedom (six-DoF) Cartesian spring. Then, the PKM 6×6 Cartesian stiffness matrix, considering the flexibility of both limb rods and motor shafts, is derived via the pertinent kinetostatic relations. Moreover, three alternative indices are defined from this model to evaluate the robot stiffness, which allows us to choose the most appropriate one for specific applications.

  • Announcing the Journal of Mechanisms and Robotics 2023 Best Paper Award
    on February 10, 2025 at 12:00 am

    JMR Best Paper for 2023

  • Active Cables Selection for Collocated Vibration Control of Small-Sized Overconstrained Cable-Driven Parallel Robots
    on February 10, 2025 at 12:00 am

    AbstractCable-driven parallel robots (CDPRs) are well appreciated for high dynamics applications, due to their lightweights moving parts. Nevertheless, due to the low stiffness of cables, vibrations can occur and can degrade performances if high precision is required, such as in additive manufacturing for instance. Previous works have studied techniques to counteract vibrations, like using motor command or embedded devices. Based on a previous first exploration of using piezoelectric transducers on cables for this type of robot, this paper presents a proper formulation of the collocated active vibration control to damp the end-effector oscillations of small-sized overconstrained CDPRs by the measure of the variation in cable tensions. This goes through a modeling of such a robot with embedded piezoelectric transducers under appropriate assumptions. From this control formulation, it is shown that the collocated nature of these transducers are fundamental. It is thus possible to highlight an energetic index of active cables selection, regardless of the used control law. The proposed technique is developed theoretically and analyzed through simulations on an eight-cable robot.

Sparse Convolution-Based 6D Pose Estimation for Robotic Bin-Picking With Point Clouds

Abstract

Estimating the orientation and position of objects is a crucial step in robotic bin-picking tasks. The challenge lies in the fact that, in real-world scenarios, a diverse array of objects is often randomly stacked, resulting in significant occlusion. This study introduces an innovative approach aimed at predicting 6D poses by processing point clouds through a two-stage neural network. In the initial stage, a network for scenes with low-textured environments is designed. Its purpose is to perform instance segmentation and provide an initial pose estimation. Entering the second stage, a pose refinement network is suggested. This network is intended to enhance the precision of pose prediction, building upon the output from the first stage. To tackle the challenge of resource-intensive annotation, a simulation technique is employed to generate a synthetic dataset. Additionally, a dedicated software tool has been developed to annotate real point cloud datasets. In practical experiments, our method demonstrated superior performance compared to baseline methods such as PointGroup and Iterative Closest Point. This superiority is evident in both segmentation accuracy and pose refinement. Moreover, practical grasping experiments have underscored the method’s efficacy in real-world industrial robot bin-picking applications. The results affirm its capability to successfully address the challenges produced by occluded and randomly stacked objects.

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