Autonomous robots can contribute to many real-world applications, such as emergency response and search-and-rescue. For some applications, however, the cost of deploying large robots may be too high, and small robots are preferred. A common issue with such small robots is navigating reliably in the presence of uncertainty. Current robot modeling and control approaches cannot capture the intricacies imposed by the effect of uncertainty at small scales. In addition, the small size restricts sensor and computational payloads, which limit the robot's perceptual and control capabilities. This project introduces a data-driven modeling framework to quantify and exploit uncertainty via control for reliable navigation of small robots. The project enables undergraduate students to become involved in research, and capitalizes on the student diversity at UC Riverside, a Hispanic-serving Institution, to broaden participation of under-represented groups.
This project investigates the mechanisms that uncertainties in robot-environment interactions affect robot behavior. Small robot motion is more stochastic since errors at the actuators and uncertain interactions with the environment amplify errors in pose. The goal is to introduce a platform-agnostic, data-driven modeling framework to quantify uncertainty and subsequently exploit it via control for reliable robot navigation under uncertainty. The specific aims are to: 1) extract dynamics using limited data for modeling uncertain systems; 2) synthesize uncertainty-aware model-based controllers based on derived reduced-order models; and 3) test and validate theoretical analysis and derived models and control algorithms with aerial, ground, and marine robots. Spectral methods are used to extract spatio-temporal dynamics and to quantify uncertainty. A model-reference adaptive control scheme utilizes extracted dynamics and uncertainty for reliable robot navigation. While the basic principles developed in this research are grounded on small robots, this project's findings may generalize to larger robots with limited sensing and noisy actuation.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
