Autonomous Decision Making with Uncertainty for an Urban Intelligence, Surveillance and Reconnaissance (ISR) Scenario
Raymond Holsapple, John Jay Baker, Phillip Chandler, Anouck Girard, Meir N. Pachter · AIAA Guidance, Navigation, and Control Conference and Exhibit · 2008
In this paper, we consider an urban ISR scenario where a human operator is tasked to provide feedback regarding the nature of some objects of interest. The feedback is relayed to the stochastic controller of an unmanned aerial vehicle (UAV), which must determine an appropriate mission plan. A small (unmanned) aerial vehicle (SAV) loiters at a high altitude where it may survey a large territory. An operator decides which objects in the SAV’s field of view are of interest and which are not. Then a team of micro (unmanned) aerial vehicles (MAVs) is assigned individual tours to inspect the objects of interest at a low altitude. As a MAV flies over an object of interest, the operator must decide if the object has a feature that uniquely distinguishes it as a target. The key parameters are the operator’s response and the time taken for the operator to respond. The controller uses these parameters to compute the expected information gain of a revisit. In previous studies automatic target recognition (ATR) was used for making some decisions in the SAV and the MAVs. This paper investigates the use of human feedback alone for target recognition. Different methods for calculating expected information gain are examined and compared. In addition, results from a flight test of this controller are presented.