Planning under uncertainty using bayesian nonparametric models

The ability to plan actions autonomously to achieve predefined objectives in the presence of environmental uncertainties is critical to the success of many Unmanned Aerial Vehicle missions. One way to plan in the presence of such uncertainties is by learning a model of the environment through Bayesian inference, and using this model to improve the predictive capability of the planning algorithm. Traditional parametric models of the environment, however, can be ineflective if the data cannot be explained using an a priori fixed set of parameters. In Bayesian nonparametric models (BNPs), on the other hand, the number of parameters grows in response to the data. This paper investigates the use of BNPs in the context of planning under uncertainty. Two illustrative planning examples are used to demonstrate that the additional flexibility of BNPs over their parametric counterparts can be leveraged to improve planning performance and to provide the capability to identify and respond to unforeseen anomalous behaviors within the environment.