A gradient descent approach algorithm for geometrical area coverage using UAVs

The rapid advancement of unmanned aerial vehicles (UAVs) has opened up new possibilities in surveillance, reconnaissance, and mapping. Efficient area coverage using UAVs is a critical challenge in these applications. This paper presents a novel algorithm for area coverage using UAVs, where each UAV leverages on the interaction with its neighbors to reach coverage convergence faster. The algorithm utilizes computational geometry techniques to estimate the uncovered area and guides UAVs to optimal positions using gradient descent, incorporating the Hessian for improved convergence. Using simulations and real drone experiments, a study of the different variables of the algorithm and their effects on the effectiveness of the system has been presented.