Latest Papers

ASME Journal of Mechanisms and Robotics

  • Ranking Static Balancing Methods Based on the Actuating Frictional Effort
    on April 17, 2025 at 12:00 am

    AbstractWhen a linkage is statically balanced, the effort required to actuate it quasi-statically in the absence of friction is zero. This is true irrespective of how the static balancing is accomplished. However, the effort is required to actuate the linkage when the Coulomb friction is present in the joints. This article shows that different static balancing methods lead to different magnitudes of the actuating frictional efforts. We further show that there exists a class of static balancing ways where between any two ways, one of the ways has a distinctively smaller magnitude of the actuating frictional effort for all values of the actuating kinematic variable. Hence, in such a case, the ways of static balancing can be ranked based on the magnitude of the actuating frictional effort. This has practical relevance when a statically balanced linkage has the Coulomb friction in its joints. Furthermore, we demonstrate that a smaller magnitude of the actuating frictional effort can be correlated to a smaller magnitude of the joint reaction forces. Thus, the magnitude of the actuating frictional effort can be used to assess the magnitude of the joint reaction forces irrespective of whether the friction in the joints is real or numerically simulated.

  • Instant Grasping Framework of Textured Objects Via Precise Point Matches and Normalized Target Poses
    on April 17, 2025 at 12:00 am

    AbstractTo reliably manipulate previously unknown objects in semi-structured environments, robots require rapid deployments and seamless transitions in pose estimation and grasping. This work proposes a novel two-stage robotic grasping method that instantly achieves accurate grasping without prior training. At the first stage, depth information and structured markers are utilized to construct compact templates for packaged targets, reducing noise and automating annotations. Then, we conduct coarse matching and design a new variant of the iterative closest point algorithm, named adaptive template-based RANSAC and iterative closest point (ATSAC-ICP), for precise point cloud registration. The method extracts locally well-registered pairs, regresses and optimizes six-degree-of-freedom (6-DOF) pose to satisfy confidence probability and precision threshold. The second stage normalizes the target pose for consistent grasp planning, which is based on scene and placement patterns. The proposed method is evaluated by several sets of experiments using various randomly selected textured objects. The results show that the pose errors are approximately ±2 mm, ±3 deg, and the successful grasping rate is over 90%. Physical experiments, conducted in different lighting conditions and with external disturbances, demonstrate effectiveness and applicability in grasping daily objects.

  • Improving Exoskeleton Brace Design: Alleviating Misalignment and Parasitic Forces
    on April 17, 2025 at 12:00 am

    AbstractThis article presents a design methodology for exoskeleton-user connection attachments, i.e., braces that aim to reduce parasitic forces and potentially improve user comfort. The proposed brace structure incorporates additional passive joints, identified through a hyperstaticity analysis to minimize undesired tangential forces, e.g., rubbing against the user’s skin. To assess the proposed structure, we primarily conducted simulation experiments using a human-exoskeleton coupled model in an MSC ADAMS environment. Subsequently, a series of real-life experiments was conducted using a self-balancing bipedal exoskeleton with two distinct dummy manikins. The results demonstrated the feasibility of the proposed brace structure in reducing the parasitic forces and slippage compared to the conventional fixation approach.

A Comparative Analysis of Optimal and Biomechanical Torque Control Strategies for Powered Knee Exoskeletons in Squat Lifting

Abstract

Exoskeletons have the ability to aid humans in physically demanding and injury-prone activities, such as lifting loads while squatting. However, despite their immense potential, the control of powered exoskeletons remains a persistent challenge. In this study, we first predict the human lifting motion and knee joint torque using an inverse dynamics optimization formulation with a two-dimensional (2D) human skeletal model. The design variables are human joint angle profiles. The normalized human joint torque squared is minimized subject to physical and lifting task constraints. After that, the biomechanical assistive knee exoskeleton torque is obtained by scaling the predicted human knee joint torque. Second, we also present a 2D human skeletal model with a powered knee exoskeleton for predicting the optimal assistive torque and lifting motion. The design variables are human joint angle profiles and exoskeleton motor current profiles. Then, the biomechanical and optimal exoskeleton torques are implemented in a powered knee exoskeleton in real-time to provide external assistance in human lifting motion. Finally, the biomechanical and optimal assistive exoskeleton torque controls for lifting are compared. It is observed that both control methods have a significant impact on reducing muscle activations for the specific muscle groups compared to the cases without the exoskeleton. Especially, peak activations of erector spinae and rectus femoris muscles are reduced by 57.79% and 47.26% with biomechanical assistive torque. Likewise, vastus medialis and vastus lateralis activations drop by 46.82% and 52.24% with optimal assistive torque.

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