Date of Award
Bachelor of Science
tensegrity, robotics, optimization, control systems, Bayesian optimization
We design and implement a new, modular, more complex tensegrity robot featuring data collection and wireless communication and operation as well as necessary accompanying research infrastructure. We then utilize this new tensegrity to assess previous research on using Bayesian optimization to generate effective forward gaits for tensegrity robots. Ultimately, we affirm the conclusions of previous researchers, demonstrating that Bayesian optimization is statistically significantly (p < 0:05) more effective at discovering useful gaits than random search. We also identify several flaws in our new system and identify means of addressing them, paving the way for more effective future research.
Boggs, James, "Optimizing Tensegrity Gaits Using Bayesian Optimization" (2018). Honors Theses. 1590.