A Novel Bias-TSP Algorithm for Maritime Patrol

Abstract

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 electro-optical 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 their length. 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.

Keywords

• Optimization methods
• design of experiments
• traveling salesman problem (TSP)
• decision support systems
• drones