COVERAGE PATH PLANNING FOR DRONES MINIMIZING ENERGY CONSUMPTION

In this study, the problem of Coverage Path Planning (PCC) is addressed, which aims to find the best path for a drone so that a given area is completely covered. Arbitrary areas containing obstacles are considered. Battery power is the main feature that limits a drone's flight time. To minimize the energy consumption of the drone, the minimization of the total distance traveled, the variations in altitude and the turns made during its route are considered. Considered NP-hard, PCC is modeled as a Traveling Salesman's Problem, where the drone must visit all points that are not obstacles. In this work, we propose a heuristic based on the GRASP meta-heuristic. The solutions determined by the proposed heuristic and the Mixed Integer Linear Programming model (solved by Gurobi) are compared. The results show that the GRASP algorithm determines excellent quality solutions while spending significantly lower CPU times.