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- 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
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- Creator:
- Chen, Yuxi
- Description:
- We use the MHD with embedded particle-in-cell model (MHD-EPIC) to study the Geospace Environment Modeling (GEM) dayside kinetic processes challenge event at 01:50-03:00 UT on 2015-11-18, when the magnetosphere was driven by a steady southward IMF. In the MHD-EPIC simulation, the dayside magnetopause is covered by a PIC code so that the dayside reconnection is properly handled. We compare the magnetic fields and the plasma profiles of the magnetopause crossing with the MMS3 spacecraft observations. Most variables match the observations well in the magnetosphere, in the magnetosheath, and also during the current sheet crossing. The MHD-EPIC simulation produces flux ropes, and we demonstrate that some magnetic field and plasma features observed by the MMS3 spacecraft can be reproduced by a flux rope crossing event. We use an algorithm to automatically identify the reconnection sites from the simulation results. It turns out that there are usually multiple X-lines at the magnetopause. By tracing the locations of the X-lines, we find the typical moving speed of the X-line endpoints is about 70~km/s, which is higher than but still comparable with the ground-based observations.
- Keyword:
- MHD, PIC, and simulation
- Citation to related publication:
- Chen, Y., Tóth, G., Hietala, H., Vines, S. K., Zou, Y., Nishimura, Y., Silveira, M. V. D., Guo, Z., Lin, Y., & Markidis, S. (2020). Magnetohydrodynamic With Embedded Particle-In-Cell Simulation of the Geospace Environment Modeling Dayside Kinetic Processes Challenge Event. Earth and Space Science, 7(11), e2020EA001331. https://doi.org/10.1029/2020EA001331 and Chen, Yuxi, et al. "Magnetohydrodynamic with embedded particle-in-cell simulation of the Geospace Environment Modeling dayside kinetic processes challenge event." arXiv preprint arXiv:2001.04563 (2020). https://arxiv.org/abs/2001.04563
- Discipline:
- Science
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- 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