An operational planning procedure for a time-critical maritime unmanned aerial vehicle (UAV) search mission is introduced and evaluated. The mission is the fast identification of a target vessel. The triggering report only contains information regarding the category and displacement of a vessel carrying out a prohibited activity, resembling operational situations. A neural network trained to classify vessels is combined with vessel clustering to reduce waypoints in the flight plan. The UAV's onboard sensors provide input for the neural network regarding each vessel in the search area, resulting in a prioritization of vessels to be visited. As the accuracy of the classification and the possibilities for clustering depend on several operational factors as well as on the UAV's sensor degradation, we investigate three methodologies to identify which planning procedure to use in various operational situations. The results show that our robust and agile approach can help a UAV find the unknown target vessel as soon as possible.INDEX TERMS Artificial intelligence, optimization methods, unmanned aerial vehicles (UAV), decision support systems.

This work aims to develop a search planning strategy to be used by a drone equipped with an inverse synthetic-aperture radar (ISAR) and an electro-optical sensor. After describing the specifics of our maritime scenario, we discuss four methodologies that can be used to find vessels involved in illegal fishing activities as quickly as possible. In addition to the clustering of the vessels, determined by the drone's electrooptical sensor range, we introduce a novel technique to bias a traveling salesman problem (TSP) tour. This bias is based on deliberately increasing distances to vessels that are classified as probable fishing vessels. This increase in distance is meant to prioritize visits to probable fishing vessels. Vessels are classified based on length using the. The classification result and the vessel clustering are available before the actual planning of the tour. Simulations of scenarios in which we have a few vessels fishing illegally show that the novel technique, the bias-TSP, combined with a tour orientation based on operational considerations, outperforms the classic TSP: the mean distance traveled to find all the vessels involved in illegal fishing activities is reduced by at least 35-50%. We also show that different drone take-off locations significantly impact the results.INDEX TERMS Optimization methods, design of experiments, traveling salesman problem (TSP), decision support systems, drones.