Animal Motion Analysis and Approximation for Robotics

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.