Latest Papers

ASME Journal of Mechanisms and Robotics

  • Mechanical Characterization of Supernumerary Robotic Tails for Human Balance Augmentation
    on August 31, 2023 at 12:00 am

    AbstractHumans are intrinsically unstable in quiet stance from a rigid body system viewpoint; however, they maintain balance, thanks to neuro-muscular sensory control properties. With increasing levels of balance related incidents in industrial and ageing populations globally each year, the development of assistive mechanisms to augment human balance is paramount. This work investigates the mechanical characteristics of kinematically dissimilar one and two degrees-of-freedom (DoF) supernumerary robotic tails for balance augmentation. Through dynamic simulations and manipulability assessments, the importance of variable coupling inertia in creating a sufficient reaction torque is highlighted. It is shown that two-DoF tails with solely revolute joints are best suited to address the balance augmentation issue. Within the two-DoF options, the characteristics of open versus closed loop tails are investigated, with the ultimate design selection requiring trade-offs between environmental workspace, biomechanical factors, and manufacturing ease to be made.

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


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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