Algorithms for Planning under Uncertainty in Prediction and Sensing

For mobile robots, uncertainty is everywhere. Wheels slip. Sensors are affected by noise. Obstacles move unpredictably. Truly autonomous robots (and decision-makers or agents in general) must act in ways that are robust to these sorts of failures and unexpected events which we may think of in general as uncertainty. In this chapter, we attempt to meet uncertainty head-on by explicitly modeling it and reasoning about it. We use the term decision theoretic planning to refer to this broad class of planning methods characterized by explicit accounting for uncertainty. We will consider a number of formulations for the problem of planning under uncertainty and present algorithms for planning under these formulations. Uncertainty can take many forms, but for brevity and clarity we will restrict our attention to only two important types: