One of the greatest challenges and frustrations arising from uncertainty is the burden of modeling. Although robots and sensors may have imperfect or incomplete information of the environment, many approaches attempt to model everything probabilistically and then design filters that attempt to estimate likely states.This course will introduce some techniques that in some contexts may allow the modeling burden to be completely avoided by carefully studying the amount of information that is minimally necessary to achieve some task. A mathematical framework will be presented for modeling cheap, minimalist sensors, which can then be used for a variety of tasks, such as exploration, navigation, tracking, monitoring, and security.The approach relies on the introduction of powerful, new combinatorial filters, which are a minimalist analog of common techniques such as Bayesian or Kalman filters. Once minimal information requirements are understood, simple, cheap robot systems can be constructed that are robust to uncertainties that never need to be explicitly handled. Furthermore, one can place probabilistic models over the minimalist structures to obtain even greater robustness in practice. Therefore, the methods from this course are compatible and complementary to common probabilistic techniques used in robotics and sensor networks.
From Planning Algorithms, S. M. LaValle, Cambridge University Press, 2006: