Information-Oriented and Energy-Aware Path Planning for Multiple Air-Dropped Small Unmanned Aerial Vehicles

The use of small Unmanned Aerial Vehicles (UAVs) has revolutionized operations such as Search and Rescue (SAR) or surveillance and monitoring. The problem remains of how to best utilize these assets given their limited endurance while taking advantage of prior target position information. The goal of this paper is to maximize the likelihood of quickly locating targets by utilizing a novel formulation of the Coverage Path Planning (CPP) problem, and applying a global optimization algorithm. The approach described takes advantage of the availability of multiple UAVs that can be air-dropped anywhere in the Area of Interest (AOI). This is particularly useful for scenarios where the AOI cannot be completely covered by a single UAV due to its limited endurance, and where the expected target location is represented by a Probability of Containment (POC) map. By prioritizing areas with a high POC, the algorithm increases the probability of target detection and reduces detection time, thereby enhancing the likelihood of survival in SAR missions. The presented algorithm outperforms the baseline Boustrophedon algorithm typically used for area coverage.