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.

Locomotion Identification Method of One-Degree-of-Freedom Six-Bar for Jumping Robot

Abstract

Exploring the locomotion of creatures is a challenging task in bionic robots, and the existing iterative design methods are mainly based on one or two characteristics to optimize robots. Here, we introduce the thinking of system identification theory to bionic robots, bypassing the exploration of the dynamics and reducing the difficulty of design greatly. A one-degree-of-freedom (DOF) six-bar mechanism (Watt I) was designated as the model to be identified, and it was divided into two parts, i.e., a one-DOF four-bar linkage and a three-DOF series arm. Then, we formed constraints and a loss function. The parameters of the model were identified based on the kinematic data of a jumping marmoset, an animal chosen for its unusually high mass-specific power output. As a result, we obtained the desired model. Then, a prototype derived from the model was fabricated, and the experiments verified the effectiveness of the method. Based on the success of our experiments, we believe our method can be applied to emulate other motions as well.

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