Latest Papers

ASME Journal of Mechanisms and Robotics

  • Statically Balancing a Reconfigurable Mechanism by Using One Passive Energy Element Only: A Case Study
    by Kuo C, Nguyen V, Robertson D, et al. on April 19, 2021 at 12:00 am

    AbstractThis paper presents the static balancing design of a special reconfigurable linkage that can switch between two one-degree-of-freedom (DoF) working configurations. We will show that the studied dual-mode linkage only requires one mechanical spring or one counterweight for completely balancing its gravitational effect in theory at both modes. First, the theoretical models of the spring-based and the counterweight-based designs are derived. The proposed design concepts were then demonstrated by a numerical example and validated by software simulation. Experimental tests on both designs were also performed. The result of this study shows that a reconfigurable mechanism with N working configurations can be completely statically balanced by using less than N passive energy elements.

  • Multiparameter Real-World System Identification Using Iterative Residual Tuning
    by Allevato A, Pryor M, Thomaz AL. on April 19, 2021 at 12:00 am

    AbstractIn this work, we consider the problem of nonlinear system identification using data to learn multiple and often coupled parameters that allow a simulator to more accurately model a physical system or mechanism and close the so-called reality gap for more accurate robot control. Our approach uses iterative residual tuning (IRT), a recently developed derivative-free system identification technique that utilizes neural networks and visual observation to estimate parameter differences between a proposed model and a target model. We develop several modifications to the basic IRT approach and apply it to the system identification of a five-parameter model of a marble rolling in a robot-controlled labyrinth game mechanism. We validate our technique both in simulation—where we outperform two baselines—and on a real system, where we achieve marble tracking error of 4% after just five optimization iterations.

  • Exploiting Redundancies for Workspace Enlargement and Joint Trajectory Optimization of a Kinematically Redundant Hybrid Parallel Robot
    by Wen K, Gosselin C. on April 19, 2021 at 12:00 am

    AbstractIn this paper, possibilities for workspace enlargement and joint trajectory optimization of a (6 + 3)-degree-of-freedom kinematically redundant hybrid parallel robot are investigated. The inverse kinematic problem of the robot can be solved analytically, which is a desirable property of redundant robots, and is implemented in the investigations. A new method for detecting mechanical interferences between two links which are not directly connected is proposed for evaluating the workspace. Redundant degrees-of-freedom are optimized in order to further expand the workspace. An approach for determining the desired redundant joint coordinates is developed so that a performance index can be minimized approximately when the robot is following a prescribed Cartesian trajectory. The presented approaches are readily applicable to other kinematically redundant hybrid parallel robots proposed by the authors.

Local and Trajectory-Based Indexes for Task-Related Energetic Performance Optimization of Robotic Manipulators

Abstract

In this paper, a task-dependent energetic analysis of robotic manipulators is presented. The proposed approach includes a novel performance index, which relates the energy consumption of a robotic manipulator to its inertia ellipsoid. To validate the method, the dynamic and electro-mechanic models of a three degrees-of-freedom (3-DOF) SCARA robot are implemented and the influence of the location of a predefined point-to-point task (such as a pick-and-place operation) within the robot workspace is considered. The task-dependent analysis provides energy consumption maps that are compared with the prediction of the theoretical formulation based on the proposed trajectory energy index (TEI), which can be used to optimally locate the task to obtain minimal energy consumption without having to compute it through extensive dynamic simulations. Results show the effectiveness of the method and the good agreement between the TEI and the effective energy consumption within the whole workspace of the robot for several trajectories.
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