Jared Strader

Date of Graduation


Document Type


Degree Type



Statler College of Engineering and Mineral Resources


Mechanical and Aerospace Engineering

Committee Chair

Yu Gu

Committee Member

Jason Gross

Committee Member

Guilherme Augusto Silva Pereira

Committee Member

Ali Baheri

Committee Member

Powsiri Klinkhachorn


In the real world, a robotic system must operate in the presence of motion and sensing uncertainty. This is caused by the fact that the motion of a robotic system is stochastic due to disturbances from the environment, and the states are only partially observable due noise in the sensor measurements. As a result, the true state of a robotic system is unknown, and estimation techniques must be used to infer the states from the belief, which is the probability distribution over all possible states. Accordingly, a robotic system must be capable of reasoning about the quality of the belief at future time steps to manage the growth of uncertainty by choosing the correct actions. This is problem is referred to as active localization and is the problem addressed in this dissertation.

This dissertation can be separated in three main parts. First, uncertainty metrics (such as the A-, E-, and D-optimality criteria) are analyzed, which are necessary for quantifying uncertainty associated with the belief. A metric is proposed extending the criteria that allows for efficiently quantifying uncertainty at future time steps. Second, the metrics are analyzed assuming only the most likely measurements are acquired during runtime, and a method is proposed called the D-optimality Roadmap (DORM) for motion planning assuming the belief is Gaussian. Third, heuristics are introduced for approximated the uncertainty at future time steps under the best-case and worst-case scenarios. The heuristics provide upper and lower bounds on the optimality criteria, which can be used for sampling paths pessimistically or optimistically, which can be used to quickly plan trajectories for active localization.