Latest Papers

ASME Journal of Mechanisms and Robotics

  • Stable Inverse Dynamics for Feedforward Control of Nonminimum-Phase Underactuated Systems
    on January 25, 2023 at 12:00 am

    AbstractAn enhanced inverse dynamics approach is here presented for feedforward control of underactuated multibody systems, such as mechanisms or robots where the number of independent actuators is smaller than the number of degrees of freedom. The method exploits the concept of partitioning the independent coordinates into actuated and unactuated ones (through a QR-decomposition) and of linearly combined output, to obtain the internal dynamics of the nonminimum-phase system and then to stabilize it through proper output redefinition. Then, the exact algebraic model of the actuated sub-system is inverted, leading to the desired control forces with just minor approximations and no need for pre-actuation. The effectiveness of the proposed approach is assessed by three numerical test cases, by comparing it with some meaningful benchmarks taken from the literature. Finally, experimental verification through an underactuated robotic arm with two degrees of freedom is performed.

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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