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.

Enhancing Payload Capacity With Dual-Arm Manipulation and Adaptable Mechanical Intelligence


Individual manipulators are limited by their vertical total load capacity. This places a fundamental limit on the weight of loads that a single manipulator can move. Cooperative manipulation with two arms has the potential to increase the net weight capacity of the overall system. However, it is critical that proper load sharing takes place between the two arms. In this work, we outline a method that utilizes mechanical intelligence in the form of a whiffletree. This system enables load sharing that is robust to position deviations between the two arms. The whiffletree utilizes pneumatic tool changers which enable autonomous attachment/detachment. We outline the overall design of a whiffletree for dual-arm manipulation. We also illustrate how this type of mechanical intelligence can greatly simplify cooperative control. Lastly, we use physical experiments to illustrate enhanced load capacity. Specifically, we show how two UR5 manipulators can re-position a 7 kg load. This load would exceed the weight capacity of a single arm, and we show that the average forces on each arm remain below this level and are relatively evenly distributed.
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