The dynamics of flight-initiating jumps in the common vampire bat Desmodus rotundus.

Desmodus rotundus, the common vampire bat (Phyllostomidae: Desmodontinae), exhibits complex and variable terrestrial movements that include flight-initiating vertical jumps. This ability is unique among bats and is related to their unusual feeding behavior. As a consequence of this behavior, the wing is expected to have design features that allow both powered flight and the generation of violent jumps. In this study, high-speed cine images were synchronized with ground reaction force recordings to evaluate the dynamics of jumping behavior in D. rotundus and to explore the functional characteristics of a wing operating under competing mechanical constraints. The pectoral limbs are responsible for generating upward thrust during the jump. The hindlimbs stabilize and orient the body over the pectoral limbs. The thumbs (pollices) stabilize the pectoral limb and contribute to extending the time over which vertical force is exerted. Peak vertical force can reach 9.5 times body weight in approximately 30 ms. Mean impulse is 0.0580+/-0.007 N s (mean +/- s.d., N=12), which accelerates the animal to a mean take-off velocity of 2.38+/-0.24 m s-1. A model of the muscular activity during jumping is described that accounts for the characteristic force output shown by these animals during flight-initiating jumps.

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Gastric Na+K+ATPase activity and intestinal urea hydrolysis of the common vampire bat, Desmodus rotundus

Comparative Biochemistry and Physiology Part A Physiology ◽

10.1016/s0300-9629(96)00464-1 ◽

1997 ◽

Vol 118 (3) ◽

pp. 665-669 ◽

Cited By ~ 7

Author(s):

Henry J. Harlow ◽

Eldon J. Braun

Keyword(s):

Atpase Activity ◽

Urea Hydrolysis ◽

Desmodus Rotundus ◽

The Common ◽

Hydrolysis Of ◽

Vampire Bat

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Planning to Minimize the Human Muscular Effort during Forceful Human-Robot Collaboration

ACM Transactions on Human-Robot Interaction ◽

10.1145/3481587 ◽

2022 ◽

Vol 11 (1) ◽

pp. 1-27

Author(s):

Luis F. C. Figueredo ◽

Rafael De Castro Aguiar ◽

Lipeng Chen ◽

Thomas C. Richards ◽

Samit Chakrabarty ◽

...

Keyword(s):

Muscular Activity ◽

The Body ◽

Force Output ◽

Content Type ◽

Joint Torques ◽

Shared Object ◽

Muscular Load ◽

Robot Configuration ◽

Human Robot Collaboration ◽

Muscular Effort

This work addresses the problem of planning a robot configuration and grasp to position a shared object during forceful human-robot collaboration, such as a puncturing or a cutting task. Particularly, our goal is to find a robot configuration that positions the jointly manipulated object such that the muscular effort of the human, operating on the same object, is minimized while also ensuring the stability of the interaction for the robot. This raises three challenges. First, we predict the human muscular effort given a human-robot combined kinematic configuration and the interaction forces of a task. To do this, we perform task-space to muscle-space mapping for two different musculoskeletal models of the human arm. Second, we predict the human body kinematic configuration given a robot configuration and the resulting object pose in the workspace. To do this, we assume that the human prefers the body configuration that minimizes the muscular effort. And third, we ensure that, under the forces applied by the human, the robot grasp on the object is stable and the robot joint torques are within limits. Addressing these three challenges, we build a planner that, given a forceful task description, can output the robot grasp on an object and the robot configuration to position the shared object in space. We quantitatively analyze the performance of the planner and the validity of our assumptions. We conduct experiments with human subjects to measure their kinematic configurations, muscular activity, and force output during collaborative puncturing and cutting tasks. The results illustrate the effectiveness of our planner in reducing the human muscular load. For instance, for the puncturing task, our planner is able to reduce muscular load by 69.5\% compared to a user-based selection of object poses.

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