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Filtering by: Creator Vasudevan, Ram Keyword simulation Discipline Engineering

Jointed tails enhance control of three-dimensional body rotation (dataset and code)

Creator:

Fu, Xun, Zhang, Bohao, Weber, Ceri J., Cooper, Kimberly L., Vasudevan, Ram, and Moore, Talia Y.

Description:

Tails used as inertial appendages induce body rotations of animals and robots---a phenomenon that is governed largely by the ratio of the body and tail moments of inertia. However, vertebrate tails have more degrees of freedom (e.g., number of joints, rotational axes) than most current theoretical models and robotic tails. To understand how morphology affects inertial appendage function, we developed an optimization-based approach that finds the maximally effective tail trajectory and measures error from a target trajectory. For tails of equal total length and mass, increasing the number of equal-length joints increased the complexity of maximally effective tail motions. When we optimized the relative lengths of tail bones while keeping the total tail length, mass, and number of joints the same, this optimization-based approach found that the lengths match the pattern found in the tail bones of mammals specialized for inertial maneuvering. In both experiments, adding joints enhanced the performance of the inertial appendage, but with diminishing returns, largely due to the total control effort constraint. This optimization-based simulation can compare the maximum performance of diverse inertial appendages that dynamically vary in moment of inertia in 3D space, predict inertial capabilities from skeletal data, and inform the design of robotic inertial appendages.

Keyword:

simulation, inertial maneuvering, caudal vertebrae, trajectory optimization, and reconfigurable appendages

Citation to related publication:

Xun Fu, Bohao Zhang, Ceri J. Weber, Kimberly L. Cooper, Ram Vasudevan, Talia Y. Moore. (in review) Jointed tails enhance control of three-dimensional body rotation.

Discipline:

Engineering and Science
FCAV Simulation Dataset

Creator:

Vasudevan, Ram, Barto, Charles, Rosaen, Karl, Mehta, Rounak, Matthew, Johnson-Roberson, and Nittur Sridhar, Sharath

Description:

A dataset for computer vision training obtained from long running computer simulations

Keyword:

autonomous driving, simulation, Computer Vision and Pattern Recognition, deep learning, Computer Science, object detection, and Robotics

Citation to related publication:

M. Johnson-Roberson, C. Barto, R. Mehta, S. N. Sridhar, K. Rosaen and R. Vasudevan, "Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?," 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017, pp. 746-753. Available at https://arxiv.org/abs/1610.01983 and https://doi.org/10.1109/ICRA.2017.7989092

Discipline:

Engineering

Jointed tails enhance control of three-dimensional body rotation (dataset and code)

FCAV Simulation Dataset

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Jointed tails enhance control of three-dimensional body rotation (dataset and code)

FCAV Simulation Dataset

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