As the robotic industry has matured, the study of animal motion has given rise to many robot designs. Researchers from multiple areas, such as biomechanics, control theory, and machine learning, have spent their energy and efforts making robots more realistic. The intent is that the automatic system can replace real animals and even perform certain tasks in harsh, or even dangerous environments. However, animal motions encompass a wide range of motion that depends on body geometries and various animal behaviors. From human walking to lizards crawling, from dogs running to horses pacing, many studies of motion only focus on one species or a few behaviors. An ever-increasing collection of papers are published that study animal motions for different species and motion regimes, and these are often based on video footage and motion capture data. This is particularly true for human motion research. While there are huge volumes of data acquired from motion capture and video, not many researches as of yet are using dynamical system analysis techniques such as dynamic mode decomposition, extended dynamic mode decomposition, or even Koopman method to understand and compare the motion across different species. Thus, the goal of this thesis is to further develop the methods mentioned above to analyze and characterize animal motion. The algorithms derived should apply regardless the shape of the body or the number of degrees of freedom for the joins. Using strategies from statistical learning theory and Koopman operator theory, several methods are derived and compared. The analysis culminates in a motion approximation, that subsequently could be used in robotic control to emulate an animal motion as much as possible. / Master of Science / As the robotic industry has matured, the study of animal motion has given rise to many robot designs. Researchers from multiple areas, such as biomechanics, control theory, and machine learning, devote their energy and efforts to making the robots more realistic, so that the automatic system can replace real animals and even perform certain tasks in harsher, or even dangerous environment. However, animal motion itself encompasses a wide range of motions that depend on body geometries and various behaviors. From human walking to lizards crawling, from dogs running to horses pacing, many studies of motion only focus on one species or a few behaviors. Many animal motion studies are often based on video footage and motion capture data. This is particularly true for human motion research when researchers are trying to create the gait pattern in medical research. While there are huge volumes of data acquired from motion captures and videos, not many researches as of yet are using dynamical system analysis techniques to understand and compare the motion across different species. Thus, the goal of this thesis is to use dynamical system analysis techniques and further develop the methods to analyze and characterize animal motion. Regardless of the shape of the body or the join types at different locations on the body, strategies from the theories of machine learning and dynamical system analysis are used to derive algorithms which should be applied to all animal motions. Several methods are derived and compared. The analysis culminates in a motion approximation, that subsequently could be used in robotic control to emulate an animal motion as realistic as possible.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/110459 |
| Date | 07 June 2022 |
| Creators | Liu, Bowei |
| Contributors | Mechanical Engineering, Kurdila, Andrew J., Southward, Steve C., Tarazaga, Pablo Alberto |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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