Energy-Efficient Drone Coverage Path Planning using Genetic Algorithm

Abstract

Unmanned Aerial Vehicles (UAVs) have been increasingly used in environmental sensing and surveying applications. Coverage path planning to survey an area while following a set of waypoints is required to complete a task. Due to the battery capacity, the UAV flight time is often limited. In this paper, we formulate the UAV path planning problem as a traveling salesman problem in order to optimize UAV energy. We propose a genetic algorithm to solve the optimization problem i.e. to minimize the energy consumption for the UAV to complete a task. We also consider reducing the number of turns to allow the UAV to optimize the flight path and to minimize its energy consumption. We compare the energy consumption of the proposed genetic algorithm to the greedy algorithm with different number of waypoints. Results show that our proposed algorithm consumes 2–5 times less energy than that of the greedy algorithm by reducing the number of turns while covering all the waypoints.