Maneuvering through 3D structures nimbly is pivotal to the
advancement of legged locomotion. However, few methods have been
developed that can generate 3D gaits in those terrains and fewer if
none can be generalized to control dynamic maneuvers. In this thesis,
foot placement planning for dynamic locomotion traversing
irregular terrains is explored in three dimensional space. Given
boundary values of the center of mass' apexes during the gait,
sagittal and lateral Phase Plane trajectories are predicted based on
multi-contact and inverted pendulum dynamics. To deal with the
nonlinear dynamics of the contact motions and their dimensionality, we
plan a geometric surface of motion beforehand and rely on numerical
integration to solve the models. In particular, we combine
multi-contact and prismatic inverted pendulum models to resolve feet
transitions between steps, allowing to produce trajectory patterns
similar to those observed in human locomotion. Our contributions lay
in the following points: (1) the introduction of non planar surfaces
to characterize the center of mass' geometric behavior; (2) an
automatic gait planner that simultaneously resolves sagittal and
lateral feet placements; (3) the introduction of multi-contact
dynamics to smoothly transition between steps in the rough terrains.
Data driven methods are powerful approaches in absence of accurate models. These methods rely on experimental data for trajectory regression and prediction. Here, we use regression tools to plan dynamic locomotion in the Phase Space of the robot's center of mass and we develop nonlinear controllers to accomplish the desired plans with accuracy and robustness. In real robotic systems, sensor noise, simplified models and external disturbances contribute to dramatic deviations of the actual closed loop dynamics with respect to the desired ones. Moreover, coming up with dynamic locomotion plans for bipedal robots and in all terrains is an unsolved problem. To tackle these challenges we propose here two robust mechanisms: support vector regression for data driven model fitting and contact planning, and trajectory based sliding mode control for accuracy and robustness. First, support vector regression is utilized to learn the data set obtained through numerical simulations, providing an analytical solution to the nonlinear locomotion dynamics. To approximate typical Phase Plane behaviors that contain infinite slopes and loops, we propose to use implicit fitting functions for the regression. Compared to mainstream explicit fitting methods, our regression method has several key advantages: 1) it models high dimensional Phase Space states by a single unified implicit function; 2) it avoids trajectory over-fitting; 3) it guarantees robustness to noisy data. Finally, based on our regression models, we develop contact switching plans and robust controllers that guarantee convergence to the desired trajectories. Overall, our methods are more robust and capable of learning complex trajectories than traditional regression methods and can be easily utilized to develop trajectory based robust controllers for locomotion. Various case studies are analyzed to validate the effectiveness of our methods including single and multi step planning in a numerical simulation and swing foot trajectory control on our Hume bipedal robot. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/22450 |
| Date | 25 November 2013 |
| Creators | Zhao, Ye, active 2013 |
| Source Sets | University of Texas |
| Language | en_US |
| Detected Language | English |
| Format | application/pdf |
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