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.

Zero Moment Control for Lead-Through Teach Programming and Process Monitoring of a Collaborative Welding Robot


Robots are commonly used for automated welding in many industries such as automotive manufacturing. The complexity and time required for programming present an obstacle in using robotic automation in welding or other tasks for small to medium enterprises that lack resources for training or expertise in traditional robot programming strategies. It also dictates a high level of repeated parts to offset the cost of weld programming. Collaborative robots or Cobots are robots designed for more collaborative operations with humans. Cobots permit new methods of task instruction (programming) through a direct interaction between the operator and the robot. This paper presents a model and model calibration strategy for collaborative robots to aid in teaching and monitoring welding tasks. The method makes use of a torque estimation model based on robot momentum to create an observer to evaluate external forces. The torque observer is used to characterize the friction that exists within the robot joints. These data are used to define the parameters of a friction model that combines static, Coulomb, and viscous friction properties with a sigmoid function to represent a transition between motion states. With an updated friction model, the torque observer is then used for collaborative robotic welding, first to provide a mode in which the robot can be taught weld paths through physical lead through and second a mode to monitor the weld process for expected motion/force characteristics. The method is demonstrated on a commercial robot.
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