Optimally-informative path planning for dynamic Bayesian classification

摘要

An agent, consisting of an unmanned aerial vehicle (UAV) carrying strapped-down sensors, is to examine a number of unidentified objects within a given search area, collect information, and utilize that information to classify the objects. The problem is challenging because the mission time is often limited, the agent is only provided with partial a priori information, and the amount of information that the sensor can measure is dependent on the relative position of the agent with respect to the object. Our technical approach is three-fold. First, we model the motion of the agent using a kinematic model with constant altitude. Second, we use a performance prediction model that gives the probability of target discrimination as a function of the range from the sensor to the object. Third, a linear classifier that utilizes Bayes' theorem diagnoses the status of the objects of interest while an information-theoretic measure is used to quantify the uncertainty in classification. We pose an optimal control problem that minimizes the classification uncertainty while taking differential constraints and the time history of the agent's steering decisions as the control input. We investigate whether maximizing information by choosing informative paths always minimizes the classification uncertainty.