Latest Papers

ASME Journal of Mechanisms and Robotics

  • An Improved Dual Quaternion Dynamic Movement Primitives-Based Algorithm for Robot-Agnostic Learning and Execution of Throwing Tasks
    on May 9, 2025 at 12:00 am

    AbstractInspired by human nature, roboticists have conceived robots as tools meant to be flexible, capable of performing a wide variety of tasks. Learning from demonstration methods allow us to “teach” robots the way we would perform tasks, in a versatile and adaptive manner. Dynamic movement primitives (DMP) aims for learning complex behaviors in such a way, representing tasks as stable, well-understood dynamical systems. By modeling movements over the SE(3) group, modeled primitives can be generalized for any robotic manipulator capable of full end-effector 3D movement. In this article, we present a robot-agnostic formulation of discrete DMP based on the dual quaternion algebra, oriented to modeling throwing movements. We consider adapted initial and final poses and velocities, all computed from a projectile kinematic model and from the goal at which the projectile is aimed. Experimental demonstrations are carried out in both a simulated and a real environment. Results support the effectiveness of the improved method formulation.

  • Chained Timoshenko Beam Constraint Model With Applications in Large Deflection Analysis of Compliant Mechanism
    on May 9, 2025 at 12:00 am

    AbstractAccurately analyzing the large deformation behaviors of compliant mechanisms has always been a significant challenge in the design process. The classical Euler–Bernoulli beam theory serves as the primary theoretical basis for the large deformation analysis of compliant mechanisms. However, neglecting shear effects may reduce the accuracy of modeling compliant mechanisms. Inspired by the beam constraint model, this study takes a step further to develop a Timoshenko beam constraint model (TBCM) for initially curved beams to capture intermediate-range deflections under beam-end loading conditions. On this basis, the chained Timoshenko beam constraint model (CTBCM) is proposed for large deformation analysis and kinetostatic modeling of compliant mechanisms. The accuracy and feasibility of the proposed TBCM and CTBCM have been validated through modeling and analysis of curved beam mechanisms. Results indicate that TBCM and CTBCM are more accurate compared to the Euler beam constraint model (EBCM) and the chained Euler beam constraint model (CEBCM). Additionally, CTBCM has been found to offer computational advantages, as it requires fewer discrete elements to achieve convergence.

Reliability-Based Analysis and Optimization of the Gravity Balancing Performance of Spring-Articulated Serial Robots With Uncertainties

Abstract

This article proposes a method for analyzing the gravity balancing reliability of spring-articulated serial robots with uncertainties. Gravity balancing reliability is defined as the probability that the torque reduction ratio (the ratio of the balanced torque to the unbalanced torque) is less than a specified threshold. In this paper, the reliability analysis is performed by exploiting a Monte Carlo simulation (MCS) with consideration of the uncertainties in the link dimensions, masses, and compliance parameters. A reliability-based design optimization (RBDO) method is also developed to seek reliable spring setting parameters for maximized balancing performance under a prescribed uncertainty level. The RBDO is formulated with consideration of a probabilistic reliability constraint and solved by using a particle swarm optimization (PSO) algorithm. A numerical example is provided to illustrate the gravity balancing performance and reliability of a robot with uncertainties. A sensitivity analysis of the balancing design is also performed. Lastly, the effectiveness of the RBDO method is demonstrated through a case study in which the balancing performance and reliability of a robot with uncertainties are improved with the proposed method.

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