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.

High Precision Trajectory Learning Method Based Improved Dynamic Movement Primitives for Robot Skill Learning


In robot skill learning, the higher accuracy of trajectory reproduction, the smaller difference between the trajectory reproduced by the robot and the expected trajectory, and the higher accuracy and reliability of the robot’s task execution. In robot skill learning methods based on dynamic movement primitives, the problems are found that the tail exponential decay of Gaussian kernel function and the number of shape parameters have a great influence on the accuracy of trajectory reproduction, a high precision trajectory learning method is proposed. A novel truncation processing method is proposed to eliminate the impact of tail exponential decay, and the optimization on the number of shape parameters is used to improve the approximation of the local gradient of the target forcing term, which are combined to improve trajectory reproduction accuracy. The principle of the proposed method is described in detail. The simulation and comparison experiments are performed to verify the effectiveness of the proposed method in improving trajectory reproduction accuracy. This paper makes contributions to the field of robot skill trajectory learning and provides a promising method for improving trajectory reproduction accuracy.

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